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. 2017 Jan 11;7:40268.
doi: 10.1038/srep40268.

Disrupted Global Metastability and Static and Dynamic Brain Connectivity Across Individuals in the Alzheimer's Disease Continuum

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Free PMC article

Disrupted Global Metastability and Static and Dynamic Brain Connectivity Across Individuals in the Alzheimer's Disease Continuum

Aldo Córdova-Palomera et al. Sci Rep. .
Free PMC article

Abstract

As findings on the neuropathological and behavioral components of Alzheimer's disease (AD) continue to accrue, converging evidence suggests that macroscale brain functional disruptions may mediate their association. Recent developments on theoretical neuroscience indicate that instantaneous patterns of brain connectivity and metastability may be a key mechanism in neural communication underlying cognitive performance. However, the potential significance of these patterns across the AD spectrum remains virtually unexplored. We assessed the clinical sensitivity of static and dynamic functional brain disruptions across the AD spectrum using resting-state fMRI in a sample consisting of AD patients (n = 80) and subjects with either mild (n = 44) or subjective (n = 26) cognitive impairment (MCI, SCI). Spatial maps constituting the nodes in the functional brain network and their associated time-series were estimated using spatial group independent component analysis and dual regression, and whole-brain oscillatory activity was analyzed both globally (metastability) and locally (static and dynamic connectivity). Instantaneous phase metrics showed functional coupling alterations in AD compared to MCI and SCI, both static (putamen, dorsal and default-mode) and dynamic (temporal, frontal-superior and default-mode), along with decreased global metastability. The results suggest that brains of AD patients display altered oscillatory patterns, in agreement with theoretical premises on cognitive dynamics.

Figures

Figure 1
Figure 1. Correlation matrix chart of demographic features.
Entries in the upper triangle correspond to Pearson’s correlation coefficients, and the number of “*” represents the significance level (**p < 10−2, ***p < 10−3).
Figure 2
Figure 2. Logistic regression results for sFC and dFC at the 325 IC pairs.
The results represent raw p-values were obtained from logistic regression models adjusting by gender, age, in-scanner motion and headcoil. The horizontal red line indicates the p-value threshold (0.05) in the corresponding logarithmic scale. Colorbars display regression coefficients for the different pairs.
Figure 3
Figure 3. IC pairs in the statistically significant sub-networks (sFC and dFC) from NBS.
Heatmap matrices: the entries represent F-statistics of the 28-edge network with NBS significance. Non-significant entries were set to 0. Notes: *sum of F-statistics from the statistically significant results, illustrating the relative importance of each IC; **average of “sum of F” over dFC and sFC.
Figure 4
Figure 4. Raw sFC and dFC values at the IC pairs with significance from the NBS tests.
The trends shown here corresponds to the direct measures of pairwise coupling (see Methods), whereas the test results discussed in the text show that the association between diagnostic status and dFC persists even after adjustments for gender, age, and headcoil. Notes: *coefficient of variation (standard deviation divided by mean) of the normalized phase differences (see Methods); **z-transformed value of the regularized regression coefficients.
Figure 5
Figure 5. Surrogate data test results at the whole-brain level and at edges with group differences.
Left: test of the sampled dFC values (sum of 325 edge weights) against a range of population means. Right: analysis of the dFC empirical values against their surrogate null distributions in the SCI group. The plot shows only the four edges with both statistically significant (clinical) group differences and p < 0.1 in the surrogate tests. Numbers next to the dashed line legends correspond to IC pairs. Additional details on the test procedures are described in Results.
Figure 6
Figure 6. Results of surrogate data tests for dFC in the SCI group.
Uppermost heatmap: T statistic maps from one-group T-tests (null hypothesis: dFC population mean = 0 at every edge). Only the original data was used for that plot. The upper diagonal shows only connections with statistically significant differences depending on clinical diagnose. Lowermost heatmap: using 10000 surrogate datasets, T* values were estimated, and 1 minus the proportion of times with T (original) > T* (surrogate) was considered the p-value for the presence of true dynamics. Note that the colormap corresponds to 1-p (lower p-values have warmer colors). In both heatmaps, the upper diagonal shows only connections with statistically significant differences depending on clinical diagnose. Bottom: sum of (empirical) T-statistic for each IC.
Figure 7
Figure 7. Association between metastability and diagnostic status.
Note: The trend shown here corresponds to the direct measures of metastability, whereas the test results discussed in the text show that the association between diagnostic status and metastability persists even after adjustments for gender, age and headcoil.
Figure 8
Figure 8. Parcellation-free spatial maps obtained using ICA.
(A) After applying an automatic model order selection and manually removing potential artifacts, 26 spatial components remained. (B) Activation levels derived from fMRI were set into a matrix of [150 subjects]×[202 time points]×[26 components]. Abbreviations: *, sampling time point, corresponding to the TR (here, 2638 ms); a.u., arbitrary units (of activation intensity). (C) Anatomical reference for IC pairs: each pair of ICs was assigned an arbitrary number, which is used as reference in the manuscript.
Figure 9
Figure 9. Computation of phase-based coupling parameters.
The pictures exemplify computation of pairwise phase-based coupling and metastability, for data from 4 ICs (one participant). (A) Four time series collected using FSL’s ICA pipelines. (B) The time series are band-pass filtered (0.04–0.07 Hz), to later compute the Hilbert transform. (C) Individual phases obtained from the imaginary component of the Hilbert transform. (D) Kuramoto order parameter estimated across time using the phases. (E) Pairwise phase-coupling matrices, C(t), estimated for the 4 phases, across all time points; delta phi (Δϕ) is normalized as described in Methods.

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