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. 2015 Dec 24;7:242.
doi: 10.3389/fnagi.2015.00242. eCollection 2015.

Common Effects of Amnestic Mild Cognitive Impairment on Resting-State Connectivity Across Four Independent Studies

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Free PMC article

Common Effects of Amnestic Mild Cognitive Impairment on Resting-State Connectivity Across Four Independent Studies

Angela Tam et al. Front Aging Neurosci. .
Free PMC article

Abstract

Resting-state functional connectivity is a promising biomarker for Alzheimer's disease. However, previous resting-state functional magnetic resonance imaging studies in Alzheimer's disease and amnestic mild cognitive impairment (aMCI) have shown limited reproducibility as they have had small sample sizes and substantial variation in study protocol. We sought to identify functional brain networks and connections that could consistently discriminate normal aging from aMCI despite variations in scanner manufacturer, imaging protocol, and diagnostic procedure. We therefore combined four datasets collected independently, including 112 healthy controls and 143 patients with aMCI. We systematically tested multiple brain connections for associations with aMCI using a weighted average routinely used in meta-analyses. The largest effects involved the superior medial frontal cortex (including the anterior cingulate), dorsomedial prefrontal cortex, striatum, and middle temporal lobe. Compared with controls, patients with aMCI exhibited significantly decreased connectivity between default mode network nodes and between regions of the cortico-striatal-thalamic loop. Despite the heterogeneity of methods among the four datasets, we identified common aMCI-related connectivity changes with small to medium effect sizes and sample size estimates recommending a minimum of 140 to upwards of 600 total subjects to achieve adequate statistical power in the context of a multisite study with 5-10 scanning sites and about 10 subjects per group and per site. If our findings can be replicated and associated with other established biomarkers of Alzheimer's disease (e.g., amyloid and tau quantification), then these functional connections may be promising candidate biomarkers for Alzheimer's disease.

Keywords: connectome; default mode network; fMRI; meta-analysis; mild cognitive impairment; resting-state.

Figures

Figure 1
Figure 1
Application of general linear models to connectomes. (A) The brain is functionally parcellated into K (e.g., 50) clusters generated through a clustering algorithm. (B) The connectome is a K × K matrix measuring functional connectivity between and within clusters. (C) A general linear model is used to test the association between phenotypes and connectomes, independently at each connection, at the group level. (D) In a multisite situation, independent site-specific effects are estimated and then pooled through weighted averaging (Willer et al., 2010).
Figure 2
Figure 2
Plot of the percentage of connections identified as significant by the statistical comparison between aMCI and CN across the connectome (qFDR ≤ 0.1), as a function of the resolutions selected by MSTEPS.
Figure 3
Figure 3
(A) Map of the percentage of connections associated with a given cluster and identified as significant by the statistical comparison between aMCI and CN, at a resolution of 33 clusters (qFDR ≤ 0.1). (B) Maximum absolute difference in average connectivity between aMCI and CN, across all connections associated with a cluster, at resolution 33. ΔF(r) signifies the difference in Fisher-transformed correlation values between the groups. (C) Four clusters of interest (superior medial frontal cortex, dorsomedial prefrontal cortex, striatum, middle temporal lobe) were selected out of 33 for further characterization.
Figure 4
Figure 4
Effect maps for a selection of four seeds that show effects related to aMCI at resolution 33. Effect maps reveal the spatial distribution of the changes in functional connectivity for (A) the superior medial frontal cortex, (B) the dorsomedial prefrontal cortex, (C) striatum, and (D) the middle temporal lobe. All connections shown in the maps of difference in average connectivity between aMCI and CN are significant at qFDR ≤ 0.1. For each panel, the top line maps the spatial location of the seed region in magenta, the second and third lines show the connectivity (Fisher-transformed correlation values, F(r)) between the designated seed region and the rest of the brain in CN and aMCI, respectively, and the fourth line shows a difference map between aMCI and CN [difference in Fisher-transformed correlation values, ΔF(r)]. The numbers in parentheses refer to the numerical IDs of the clusters in the 3D parcellation volume, as listed in Supplementary Table 2.
Figure 5
Figure 5
Comparisons of effects in the superior medial frontal cortex across samples. This figure illustrates functional connectivity changes between aMCI and CN, average connectivity in CN, and average connectivity in aMCI in each site (ADNI2-Achieva, ADNI2-Gemini, ADNI2-Ingenia, ADNI2-Intera, CRIUGMa, CRIUGMb, MNI) independently of other sites and when samples are pooled together (all samples). The number in parentheses refers to the numerical ID of the seed in the 3D parcellation volume, as listed in Supplementary Table 2.
Figure 6
Figure 6
Mean connectivity between (A) the superior medial frontal cortex and middle temporal lobe, (B) the dorsomedial prefrontal cortex and middle frontal gyrus, (C) the striatum and pre/postcentral gyrus, and (D) middle temporal lobe and posterior cingulate in CN and aMCI in the independent samples. Each map displays the seed (pink) and a selected cluster (blue) whose connectivity with the seed significantly differed between CN and aMCI in the pooled analysis. The box-whisker plots display the mean connectivity (Fisher-transformed correlation values) between the seed and the selected parcel, overlaid over individual data points, in the CN and MCI groups in the ADNI2-Achieva, ADNI2-Gemini, ADNI2-Ingenia, ADNI2-Intera, CRIUGMa, CRIUGMb, and MNI samples. We also report the Cohen's d (a weighted average of the effect sizes per sample) followed by a sample size estimate (for 80% power, balanced groups, bilateral tests, Gaussian distributions, and α = 0.05) in square brackets in the top-right corner of each plot. The numbers in parentheses in the titles refer to the numerical IDs of the seeds in the 3D parcellation volume, as listed in Supplementary Table 2. For box-whisker plots for all significant clusters with each of these seeds, see Supplementary Figures 6–9.

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