Fusing task and RS fMRI

In our fourth study, we wanted to investigate whether functional alterations in task and at rest were related within the same cohort of EC and MCI patients. In the first two studies, we have demonstrated that both task-evoked activation patterns and RS FC were able to distinguish between EC and MCI patients, in accordance with reports from the literature. However, few studies have investigated the two states in the same individuals, in either AD or its prodromal/preclinical stages. Fleisher and collaborators have compared task and RS fMRI for separating cognitively normal individuals with genetic risk for AD from controls (Fleisher, Sherzai et al. 2009). They did not find any difference in task-related activation between the two groups; however, they found reduced deactivation of medial and lateral parietal regions during associative encoding in at-risk participants. In RS fMRI, they found significant FC differences in nine DMN regions between the two groups, which yielded a larger effect size than task fMRI changes. In contrast, Wang and collaborators did not find any differences in DMN RS connectivity between MCI patients and EC, while they found task-related decreased connectivity between anterior and posterior brain regions during a visual encoding task (Wang, Li et al. 2013).

However, these two studies did not seek to establish a direct link between task and rest indices as

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such. Finally, Schwindt and collaborators investigated DMN connectivity in mild AD patients and EC during a task of visual detection and at rest, and found that DMN connectivity was modulated across states in controls, but not in patients (Schwindt, Chaudhary et al. 2013). In particular, while DMN regions were more activated at rest than during task in EC, their activity was not different between the two states in MCI. These data thus suggest that there is a link between functional anomalies that are detected in task-evoked states and RS, possibly mediated by DMN regions.

We used PLSC to investigate how task-related functional activity and RS FC were related in EC and MCI patients. We were interested in testing whether the relationship between the two states would persist despite pathological processes; therefore, we computed PLSC across all subjects without specifying the presence of two different groups. We hypothesized that we would find a significant task-rest link in the form of significant LV patterns. Moreover, based on the literature, we expected that one of the LVs would involve the DMN or one of its subcomponents.

Furthermore, because we are using task activity elicited by an associative memory task, and because the DMN has close ties to memory, we also hypothesized that the link between task and rest fMRI would be somehow associated with memory performance. Therefore, we tested whether the task and rest scores of significant LVs were correlated with memory measures.

2.2.4.1 Participants

Our sample was composed of 21 single-domain aMCI patients (12 females, mean age 71.9 years, 13.2 mean years of education) and 21 EC (16 females, mean age 69.7 years, 13.9 mean education years).

2.2.4.2 Neuropsychological data analysis

We replaced missing scores that resulted from not completing a test because the patient was too impaired by the lowest possible z-score, i.e., corresponding to a percentile of 1%.

2.2.4.3 Task performance analysis

We computed an analysis of variance testing the main effects of group, semantical relatedness and distractor familiarity, as well as their interactions, on task accuracy and reaction time.

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2.2.4.4 Task fMRI modeling

We tested a slightly different first-level GLM for this study. The model included regressors for the 6 experimental conditions, 2 conditions modeling instructions for the encoding and recognition phases, plus 24 motion parameters (Friston 24-parameter model)(Friston, Williams et al. 1996). We also modeled reaction time (the time it took the subject to push one of the answer buttons) as a parametric modulator for all recognition conditions. Duration of trial was the display duration of each event (i.e., 4 seconds for both encoding and recognition conditions, 4 seconds for encoding instructions and 8 seconds for recognition instructions).

Based on previous results from our group using partially overlapping data and the same protocol (van der Meulen, Lederrey et al. 2012), in which the strongest effect (apart from the group effect) was the group by distractor familiarity interaction, we chose to use this contrast for further analyses using PLSC. We defined the distractor familiarity effect as an F contrast at the individual subject level, and took the square root of the F values for further processing. Finally, we constrained all subsequent PLSC analyses to a group mask containing all the voxels with a GM probability higher than 0.1 and that were common to all subjects.

2.2.4.5 RS fMRI modeling

There were a few differences in the RS preprocessing and modeling compared to what has previously been described. After extracting region-averaged time series based on the Shirer atlas (Shirer, Ryali et al. 2012), we regressed out 24 movement parameters, as well as the average signal of a mask of CSF and WM. As the FC measure, we used pairwise Pearson correlation coefficients between the filtered timecourses (0.03–0.06 Hz) of all brain regions. Finally, we converted these correlation coefficients to z-scores using Fisher-Z transformation.

2.2.4.6 PLS

The analysis pipeline that we apply here is illustrated in Figure 11. We first reduced the dimensionality of both the task activity and the rest connectivity matrices using SVD. This first step was necessary, because the computational power required by a PLSC analysis between two matrices consisting of 44 x 45’415 (subjects x voxels in the task fMRI data) and 44 x 4’005 (subjects x Pearson correlation coefficients between all brain regions of the Shirer atlas) was too

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high in regards to available resources. Notably, contrary to the PLSC of the first study, we did not specify the presence of two groups, as we sought to find a task-rest relationship that was common across all subjects.

Figure 11. Analysis pipeline. Both the rest and task matrices are first reduced via SVD, and components explaining 90% of the variance are used for further analyses. After computing PLS between the low-dimensional task and rest matrices, task and rest scores of significant LVs are correlated with behavioral scores in a CCA.

For the task matrix decomposition, this resulted in: 𝑋_𝑡 = 𝑈_𝑡× 𝑆_𝑡× 𝑉_𝑡^𝑇, and the rest matrix decomposition: 𝑋_𝑟= 𝑈_𝑟× 𝑆_𝑟× 𝑉_𝑟^𝑇. For each modality, we computed 𝑈 × 𝑆 and normalized both matrices by z-scoring them across all subjects. We selected components that explained 90%

of the variance, resulting in 25 rest (𝑋) and 23 task (𝑌) components. The cross-correlation matrix (𝑅) was computed between 𝑋 and 𝑌, followed by SVD of 𝑅, which provides the best reconstruction of 𝑅 by low-dimensional matrices: 𝑅 = 𝑈 × 𝑆 × 𝑉^𝑇 , where 𝑈 and 𝑉 are saliences, and 𝑆 is the diagonal matrix containing the singular values. 𝑈 and 𝑉 represent respectively the rest connections and the task voxels that best characterize 𝑅 (or are most related to the effects expressed in the latent variable). Next is the creation of latent variables 𝐿_𝑥 and 𝐿_𝑦 by projecting 𝑋 and 𝑌 onto their respective saliences 𝑉 and 𝑈: 𝐿_𝑥 = 𝑋 × 𝑉 , and 𝐿_𝑦 = 𝑌 × 𝑈.

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These LVs are linear combinations of the original variables 𝑋 and 𝑌. The vectors obtained (one for the rest data and one for the task data, per LV) are called rest and task scores. PLSC searches for LVs that express the largest amount of common information to both 𝑋 and 𝑌 (maximal covariance ¹).

In this context, PLSC allowed us to identify patterns of task activity that covary with RS connectivity. These LVs are similar to principal components that optimally relate the two modalities. They can be represented in both task activity and RS connectivity dimensional spaces, in the form of saliency maps, which depict the contribution of each voxel activity/region connectivity to that LV. A subject will have a “score” in each of the modalities, which reflects the extent to which this subject expresses the LV pattern.

We assessed the statistical significance of the extracted LVs with the use of permutation testing (5’000). We estimated the contribution of each voxel/connection to the LV with a bootstrap procedure (500 bootstraps), which gives an estimate of the standard error by resampling observations in the original X and Y matrices, and dividing task and rest saliences by their standard error. Then we average the task and rest saliences obtained via bootstrapping, and project them on the original data components (𝑉_𝑟 or 𝑉_𝑡). Finally, we normalized the values by dividing the result by its standard error. For the LV visualization in the task activity space, we mapped the 10% highest and lowest values.

In a first post-hoc analysis, we ran either two-sample t-tests or Mann-Whitney U tests to test for group differences between task and rest scores of EC and MCI patients, and scores between MCIc and MCInc.

2.2.4.7 Canonical correlation analysis

In another post-hoc analysis, we computed canonical correlation analyses (CCA) between task/rest scores and behavioral/neuropsychological measures, in order to determine whether the extent to which subjects exhibit LV patterns was associated with their memory performance.

CCA is a multivariate technique that measures the linear relationship between two multidimensional variables. Briefly, CCA identifies two sets of vectors (one per variable) such

1 Given the prior z-scoring of matrices X and Y, we essentially decompose the correlation matrix.

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that the correlations between the projections of the variables on these vectors are maximized. We used the MATLAB in-built function canoncorr to compute CCA, and normalized all variables first using z-scoring.

For the first variable, we grouped together neuropsychological (Grober-Buschke immediate and delayed recall, Doors and People part A and B, digit span forward and backward) and task-related behavioral (task accuracy for conditions with a new distractor and with an old distractor) measures. In the second variable, we grouped together task and rest scores for a given LV. We ran a separate CCA for each LV, across all subjects and within each group (EC, MCI) separately.

We did not compute a separate CCA within MCIc and MCInc, because the number of MCInc was too low (N=10).

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