Temporally-independent functional modes of spontaneous brain activity

Abstract

Resting-state functional magnetic resonance imaging has become a powerful tool for the study of functional networks in the brain. Even "at rest," the brain's different functional networks spontaneously fluctuate in their activity level; each network's spatial extent can therefore be mapped by finding temporal correlations between its different subregions. Current correlation-based approaches measure the average functional connectivity between regions, but this average is less meaningful for regions that are part of multiple networks; one ideally wants a network model that explicitly allows overlap, for example, allowing a region's activity pattern to reflect one network's activity some of the time, and another network's activity at other times. However, even those approaches that do allow overlap have often maximized mutual spatial independence, which may be suboptimal if distinct networks have significant overlap. In this work, we identify functionally distinct networks by virtue of their temporal independence, taking advantage of the additional temporal richness available via improvements in functional magnetic resonance imaging sampling rate. We identify multiple "temporal functional modes," including several that subdivide the default-mode network (and the regions anticorrelated with it) into several functionally distinct, spatially overlapping, networks, each with its own pattern of correlations and anticorrelations. These functionally distinct modes of spontaneous brain activity are, in general, quite different from resting-state networks previously reported, and may have greater biological interpretability.

Fig. 1.

Three visual components from a 21-dimensional spatial ICA decomposition of the complete dataset, as well as three components from the 21-dimensional TFM analysis. To help localize the maps structurally, they are shown on the partially inflated cortical surface, with sulci indicated by darker background intensity. To help localize the maps functionally, the bottom row shows several cytoarchitecturally based (V1 and V2) and retinotopically based (higher visual) areas from the “FS_LR” atlas (11). LGN, lateral geniculate nucleus. FEF, frontal eye fields.

Fig. 2.

Spatial maps for TFMs 11 (semantic with default mode system) and 13 (language versus default mode system).

Fig. 3.

Average correlations with PCC, estimated through seed-based correlation (A) and by averaging across all TFMs (B–D). (A) PCC seed region (green) is determined based on standard-space coordinates from the DMN literature: (−5.1, −52.5, 40.7), (−2, −39, 38.2), (0, −54.9, 26.5), (−6, −58, 28). The PCC's average time series is regressed against all voxels’ time series, resulting in the average map of correlation (red-yellow) and anticorrelation (blue). (B) The 21 TFM node-weight vectors; each column shows the node weights that make up the “spatial map” for a given TFM. (C) Each vector was multiplied by its entry for node 49 (PCC), and the results were then averaged across all TFMs, giving the right-most column. This average depicts the node weights corresponding to the average correlation with PCC, and is shown as a voxelwise map (D) by multiplying the value for each node by the corresponding node's spatial map. The PCC node is in green.

Fig. 4.

A further 15 TFMs from the set of 21. The most informative view(s) for each is chosen from the left/right, medial/lateral surfaces.

Fig. 5.

TFM 15 (global versus insula mode).