sj-docx-1-ggm-10.1177_23337214241237097 – Supplemental material for Perceived Challenges and Emotional Responses in the Daily Lives of Older Adults With Disabilities: A Text Mining Study

Supplemental material, sj-docx-1-ggm-10.1177_23337214241237097 for Perceived Challenges and Emotional Responses in the Daily Lives of Older Adults With Disabilities: A Text Mining Study by Soyoung Choi in Gerontology and Geriatric Medicine</p

Temporal Non-Local Means Filtering Reveals Real-Time Whole-Brain Cortical Interactions in Resting fMRI

<div><p>Intensity variations over time in resting BOLD fMRI exhibit spatial correlation patterns consistent with a set of large scale cortical networks. However, visualizations of this data on the brain surface, even after extensive preprocessing, are dominated by local intensity fluctuations that obscure larger scale behavior. Our novel adaptation of non-local means (NLM) filtering, which we refer to as temporal NLM or tNLM, reduces these local fluctuations without the spatial blurring that occurs when using standard linear filtering methods. We show examples of tNLM filtering that allow direct visualization of spatio-temporal behavior on the cortical surface. These results reveal patterns of activity consistent with known networks as well as more complex dynamic changes within and between these networks. This ability to directly visualize brain activity may facilitate new insights into spontaneous brain dynamics. Further, temporal NLM can also be used as a preprocessor for resting fMRI for exploration of dynamic brain networks. We demonstrate its utility through application to graph-based functional cortical parcellation. Simulations with known ground truth functional regions demonstrate that tNLM filtering prior to parcellation avoids the formation of false parcels that can arise when using linear filtering. Application to resting fMRI data from the Human Connectome Project shows significant improvement, in comparison to linear filtering, in quantitative agreement with functional regions identified independently using task-based experiments as well as in test-retest reliability.</p></div

Statistical tests for improved agreement with task labels: Table of (uncorrected) p-values for signed-rank test for ‘best’ performance of LB and tNLM filtering (see text for detailed description).

<p>For each task label, the best performance parameters for both filtering approaches are reported in <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0158504#pone.0158504.g007" target="_blank">Fig 7(c)</a>. The agreement fractions across population, computed with these filtering parameters, are used as the performance metric for the tests. The alternate hypothesis “tNLM>LB” means that the median agreement fraction of the tNLM approach is greater than the median agreement fraction of the LB approach; and similarly for “LB > tNLM”.</p

Cortical map of the cumulative boundaries of N-cut parcellations over fifteen different values of <i>K</i> in a single subject with (a) LB (<i>t</i> = 4) and (b) tNLM (<i>h</i> = 0.72) filtering.

<p>The population average cumulative boundary map across the 40 subjects are also shown with (c) LB (<i>t</i> = 4) and (d) tNLM (<i>h</i> = 0.72) filtering. The value at each triangle represents total number of times that triangle was a identified as a boundary triangle across fifteen different clustering results (<i>K</i> = 2, 3, 4, 5, 6, 7, 8, 9, 10, 15, 30, 40, 50, 60, 80). The boundary maps are thresholded at an upper boundary count of 10 for single subject and 6 for the population average.</p

Illustration of smoothing effects on cortical BOLD signal intensity in rfMRI in a single subject, shown at a single time point: (a) no filtering, (b) LB filtering (<i>t</i> = 4) and (c) tNLM filtering (<i>h</i> = 0.72).

<p>Color scale shows positive (red), negative (blue) and zero (white) BOLD signal intensity. 2.5 minute real-time movies showing the un-filtered, LB- and tNLM-filtered rfMRI data can be found in the Supplemental Information (<a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0158504#pone.0158504.s002" target="_blank">S1</a>–<a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0158504#pone.0158504.s004" target="_blank">S3</a> Videos). It is difficult to detect spatial structure in the original unfiltered data, even if there are hints that can be discerned. By applying either LB or tNLM filtering, however, the noise is reduced and coherence in local activation/deactivation with respect to the underlying anatomy of the cerebral cortex is revealed. We see synchronous bilateral activity (in red) for both filtering methods in brain regions associated with the anterio-medial, posterio-medial and dorso-lateral regions of the DMN. LB filtering (b) however, shows some additional small isolated patches in the fronto-lateral cortex, anterior insula, and the post-central gyri and the mesial motor regions, as indicated by the arrows. Interestingly, most of these isolated patches lie in regions that have been reported to show strong negative correlations to the DMN [<a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0158504#pone.0158504.ref032" target="_blank">32</a>–<a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0158504#pone.0158504.ref035" target="_blank">35</a>], and so are unlikely to be synchronous with DMN regions. Similar behavior can be observed at another time point when (d) the original rfMRI data is filtered with (e) LB and (f) tNLM, where most of the DMN regions again show synchronous BOLD signal intensity in red. The tNLM results appear clearer in the sense that contrast in the images and movies appears to more closely follow discrete anatomical regions than do the LB results. The arrows in (e) show small regions of activation/deactivation in the LB filtered data that may result from smoothing across distinct functional areas. These may subsequently give rise to erroneous parcellation results as described in the text. The differences between the two methods is more readily evident in the movies of continuous resting state recording (see <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0158504#pone.0158504.s002" target="_blank">S1</a>–<a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0158504#pone.0158504.s004" target="_blank">S3</a> Videos). Note in particular the different dynamic of the changes in brain activity—LB filtered images change smoothly from one brain state to the next while the tNLM images depict a more burst like change across consecutive brain states.</p

Cortical parcellation N-cuts applied to tNLM filtered (h = 0.72) data for the same subject as in Fig 4 for (a) <i>K</i> = 15, (b) <i>K</i> = 30 and (c) <i>K</i> = 60 clusters.

<p>See Fig C in <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0158504#pone.0158504.s001" target="_blank">S1 Text</a> for equivalent images for unfiltered and LB filtered data.</p

Test-retest reliability.

<p>Median concordance of parcellation results over the pairs of rfMRI sessions (40 subject × 6 session pairs) as a function of the number of cuts, <i>K</i> = 2 to 80, for different filtering approaches. Square boxes indicate significant differences (uncorrected p-value < 0.0004) between best of tNLM and LB median concordance values, as tested with Mann-Whitney U (rank-sum) test.</p

Cortical parcellations using N-cuts on a fully connected cortical surface graph for a single subject to partition cortex into <i>K</i> = 6 networks with (a) unfiltered data, (b) LB filtering (t = 4), and (c) tNLM filtering (h = 0.72).

<p>In each case a distinct color represents one of the <i>K</i> = 6 networks. Arrows in (b) illustrate regions lying between two large parcels that are classified as a separate network and appear similar to the false regions resulting from linear smoothing shown in the simulation in <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0158504#pone.0158504.g003" target="_blank">Fig 3(b)</a>.</p

Representative frames, taken from S1 Video, illustrating dynamic brain activity at “rest”, as seen with tNLM filtering (<i>h</i> = 0.72).

<p>See <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0158504#pone.0158504.s002" target="_blank">S1 Video</a> for the complete 2.5 min movie. Each subfigure shows the BOLD intensity in left-hemisphere at a particular time-point after tNLM filtering of a 15-minutes long minimally processed rfMRI data. The brain activation regions shift dynamically from one network to another, which can be most easily noticed around the default mode network (DMN) and anti-correlated DMN. These networks consist predominantly of large regions distributed throughout the brain that are spatially separate but have near synchronous temporal activity. At 01:05, we see activity below the mean in the DMN with the rest of the brain showing mostly activity above the mean. By 1:14 we see the opposite brain activity pattern where the DMN is now above the mean. The rest of the brain shows mostly activity below the mean, with the exception of the upper half of the sensory-motor cortices (SMC) which, on the right, show some activity above the mean, mostly mesially. Only 2 seconds later, at 01:16, the lateral temporal and parietal nodes of the DMN show activity clearly below the mean, while the activity in the PMC is still above the mean, but less so, and the activity in the mesial frontal regions is now mostly below the mean; another 2 seconds later, at 01:18, all of the DMN nodes are clearly below the mean, while mesial occipital regions are above the mean; five more second have passed (01:23) and the image is almost the reverse of what was seen at 01:14; the DMN nodes show clear negativity, as does SMC, while the rest of the brain, including the insulae, is above the mean; after 10 seconds, at 01:33, the frontal lobe, a small area of the SMC and the insula are above the mean, while the remainder of the hemisphere is below the mean; after another 3 seconds, at 01:36, the DMN nodes are well above the mean (more so than at 01:45), the occipital lobe below the mean, and the insula and dorsolateral SMC close to the mean, but the mesial motor cortices well above the mean; at 01:49 the DMN nodes are negative while the SMC shows activity clearly above the mean, and so do the occipital lobe and the insula; two seconds later, at 01:51, the brain activity is in general above the mean with three interesting exceptions: the PMC, the angular gyrus and the insula show activity below the mean; five seconds later, at 01:56, the brain is massively negative with a few exceptions where the activity approximates the mean; and at 01:59 the DMN nodes again start to show activity above the mean.</p

Quantitative comparison with task labels.

<p>(a) An example of task labels for a single subject obtained from 4mm smoothed task fMRI data (see <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0158504#sec002" target="_blank">Methods</a> section for task-pair for each label-ID) (b) Mean agreement fraction, across 40 subjects × 4 sessions, of an example task label (Left foot, motor task) with N-cuts parcellations obtained using unfiltered, LB filtered and tNLM filtered rfMRI data. See Fig K in <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0158504#pone.0158504.s001" target="_blank">S1 Text</a> for corresponding plots for all task labels. (c) Best performance of different filtering approaches across different task labels. For each task and each filtering approach, we select the parameters which achieves the highest mean agreement fraction. The grouped bar plot shows the highest mean agreement fraction and the text on top shows the corresponding parameters.</p