Functional connectomics from resting-state fMRI - PubMed (original) (raw)
Review
. 2013 Dec;17(12):666-82.
doi: 10.1016/j.tics.2013.09.016. Epub 2013 Nov 12.
Diego Vidaurre, Christian F Beckmann, Matthew F Glasser, Mark Jenkinson, Karla L Miller, Thomas E Nichols, Emma C Robinson, Gholamreza Salimi-Khorshidi, Mark W Woolrich, Deanna M Barch, Kamil Uğurbil, David C Van Essen
Affiliations
- PMID: 24238796
- PMCID: PMC4004765
- DOI: 10.1016/j.tics.2013.09.016
Review
Functional connectomics from resting-state fMRI
Stephen M Smith et al. Trends Cogn Sci. 2013 Dec.
Abstract
Spontaneous fluctuations in activity in different parts of the brain can be used to study functional brain networks. We review the use of resting-state functional MRI (rfMRI) for the purpose of mapping the macroscopic functional connectome. After describing MRI acquisition and image-processing methods commonly used to generate data in a form amenable to connectomics network analysis, we discuss different approaches for estimating network structure from that data. Finally, we describe new possibilities resulting from the high-quality rfMRI data being generated by the Human Connectome Project and highlight some upcoming challenges in functional connectomics.
Keywords: connectomics; network modelling; resting-state fMRI.
Copyright © 2013 Elsevier Ltd. All rights reserved.
Figures
Figure 1
Illustration of the main steps that take rfMRI data (with an activity timeseries at every point in the brain), identify network nodes, and then estimate network edges.
Figure 2
Examples of different approaches for connectivity analysis. All results are derived from group-average connectivity from the first 131 subjects’ rfMRI data released publicly by the HCP (available from humanconnectome.org) and displayed on lateral and medial views of the inflated left hemisphere. A and B show large-scale “networks” represented by extended spatial maps; A shows a single seed-based correlation map, with the seed (marked with a dot) placed in the precuneus, whereas B shows the spatial map from a single component from a low-dimensional (30) ICA decomposition. C and D show high-dimensional decompositions of the data into hundreds of network nodes; C shows an exemplar “hard” parcellation of the grey matter into hundreds of non-overlapping parcels, whereas D shows several ICA components from a high-dimensional (300) ICA decomposition. Seed-correlation maps may contain negative values (seen here in blue/purple), which indicate cortical regions that are anticorrelated over time with the seed. Likewise, ICA components’ spatial maps can contain negative values, which indicates anticorrelation with that component’s associated timeseries. In the case of high-dimensional parcellations, anticorrelations would likely be seen not in the spatial maps, but as negative correlations between the timeseries associated with different parcels. The cortical surface renderings were generated using the Connectome Workbench display tool (humanconnectome.org/connectome/connectome-workbench.html).
Figure 3
Schematic of relationships between various network modelling analyses for/from fMRI, adapted from Smith12b
Figure 4
Functional connectomes, estimated for 98 nodes derived from 131 HCP subjects’ rfMRI data using group-ICA (with FSL’s MELODIC tool). A) Full and partial correlation matrices. Full correlation is shown below the diagonal, and partial correlation above. Intersubject alignment was carried out using the MSM method (see Fig. 5). Small images at the top of each column summarize each node’s spatial map. The nodes were reordered according to a hierarchical clustering of the full correlation matrix (using Ward’s method as implemented in MATLAB), visualized at the top. B) An expanded view of the top-left part of A; the individual nodes’ spatial maps can now be more clearly seen; these two clusters involve default-mode [13] and language areas. C) The second of these clusters – covering the default-mode network – is shown in an alternative representation of nodes and edges, derived from the thresholded (|Z|>10) group-level partial correlation matrix. The partial correlation values are displayed in terms of their strength (connection line thickness) and sign (colour; positive connections shown in red). Network analysis and visualization is carried out using the FSLNets package (fsl.fmrib.ox.ac.uk/fsl/fslwiki/FSLNets), with (C) created using Graphviz (
).
Figure 5
Example analyses showing the effects of improved surface alignment across subjects. A) Similarities between pairs of subjects’ network matrices. All 8417 pairings of non-related subjects in the HCP data were used, after application of 3 different cross-subject surface alignment methods. All 3 group means are statistically significantly different from each other. B) Group-level analyses of two tasks from 120 HCP subjects’ task-fMRI datasets. Above is shown the group maps when subjects are aligned with each other using just the folding information. Below is shown the group maps when MSM utilizes multimodal information, including resting-state networks. The arrows mark example regions showing improved spatial localization of activation.
Figure 6
Illustration of how multiple subjects’ network matrices can be combined and modelled against other subject variables. A) The elements of the partial correlation matrix from each subject are re-ordered into a single row, in order to prepare for combining all network matrix estimates across subjects. B) The resultant Nsubjects × Nedges matrix represents all parcellated connectomes from all subjects, which can be used to relate the parcellated connectome to subjects’ behaviour, genotype and other personal measures.
Figure 7
A significant association was found between network matrices and fluid intelligence (FI), p<0.05 (corrected for multiple comparisons across all 91 behavioural variables tested). The 4753 edges were reduced using a Bayesian feature selection method [81] that only kept the 98 edges most strongly predictive of FI, and applied ridge regression (L2) shrinkage on those kept features (see Box 2); over-fitting was avoided by carrying out the feature selection and shrinkage inside a leave-one-out loop. Statistical significance was estimated using subject-wise permutation testing to derive p-values on the model fitting, taking into account the family structure in the data, such that cross-subject correlations were correctly handled. A) The 24 edges (node-pairs) that (on average) had the strongest weights in the regression are shown. The coloured bars connecting the two nodes in each pair reflect the overall group-average connection strength. Each edge’s weight in the multiple regression is noted as the “value”. B) The node-pair which contributes most strongly to the regression against FI is shown in more detail; left hippocampus (generally associated with memory/recall) and medial/lateral frontal regions (generally associated with cognitive control). C) Predicted vs. measured FI, with one data point per subject; each subject’s FI was predicted using their network matrix, where the linear regression model was trained excluding the data from that subject (and all of their family members).
Figure 8
Relationship between edge strength and sex. We discarded the second subject of any pair of twins (leaving 104 subjects), to avoid the danger of overfitting (in the case of sex prediction) or confounding the statistical inference (when carrying out the univariate t-tests), given that twins are likely to have similar network matrices to each other. A) The 24 edges which are most different in connection strength between males and females are shown; the first 9 are significantly different, p<0.05, two-tailed, corrected for multiple comparisons using FSL’s randomise tool. The coloured bars connecting the two nodes in each pair reflect the overall connection strength, averaged across all subjects of both sexes. B) The two nodes whose connection (edge) is most different between the sexes are shown in greater detail: posterior cingulate/precuneus and frontal pole. C) For this edge, the connection strengths in the two groups are shown as separate cross-subject distributions.
Figure 9
Two “Temporal Functional Modes” (TFMs) [42], estimated from 131 subjects’ data from the HCP, with cross-subject alignment carried out using MSMmultimodal. 98 nodes’ timeseries were fed into temporal-ICA and 98 ICA components were estimated. Split-half reproducibility testing ([89], running temporal-ICA separately on two halves of the complete dataset, with subjects randomly assigned to one group or the other) indicated that 29 of the estimated TFMs were significantly spatially reproducible. Each TFM spatial map was generated by multiplying each node’s voxelwise spatial map (obtained by the original 100-dimensional group-level spatial-ICA) by the weight identified by the temporal-ICA for that node for that TFM, and summing across all such weighted node maps. A) Split-half reproducibility for TFM 12. The two plots (differently coloured, for the different sub-groups of subjects) show the set of node weights for this TFM; this is the temporal-ICA “mixing matrix” and determines the TFM’s spatial map. B) Spatial map of TFM 12 on inflated cortical surfaces. C) An activation contrast map from the HCP task-fMRI datasets, with a specific task chosen that matches spatially the TFM found from the resting data. This TFM closely matches the 2-back vs. 0-back contrast in the HCP working memory task. Black dots in B and C are in the same anatomical location in the two cases, and placed to be centred on TFM 12 patches. D) Split-half reproducibility for TFM 36. E) Spatial map of TFM 36. F) Task-fMRI activation contrast map for the ‘story vs. maths’ contrast, comparing listening to stories vs. answering spoken arithmetic questions; in this case the TFM analysis has identified a functional network showing great similarity to language function. Black dots in E and F are centred on TFM 36 regions. Green arrows indicate TFM patches that are spatially more focal than the corresponding task-fMRI activations. It is clear that while sharing several nodes, these two TFMs have a different overall spatial makeup than each other, and are reproducible across the two sets of distinct subjects. See also the [Supplemental_TFMs_Movie], which shows TFM brain dynamics from 10 subjects, one after another. For each subject, 1 minute of dynamics is shown, at X4 real time. Different colours are different TFMs; for a given TFM, darker clusters are anticorrelated with brighter clusters (
www.fmrib.ox.ac.uk/\~steve/ftp/TFMs.mov
).
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References
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