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. 2017 May;38(5):2683-2708.
doi: 10.1002/hbm.23553. Epub 2017 Mar 10.

Identifying Dynamic Functional Connectivity Biomarkers Using GIG-ICA: Application to Schizophrenia, Schizoaffective Disorder, and Psychotic Bipolar Disorder

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Free PMC article

Identifying Dynamic Functional Connectivity Biomarkers Using GIG-ICA: Application to Schizophrenia, Schizoaffective Disorder, and Psychotic Bipolar Disorder

Yuhui Du et al. Hum Brain Mapp. .
Free PMC article

Abstract

Functional magnetic resonance imaging (fMRI) studies have shown altered brain dynamic functional connectivity (DFC) in mental disorders. Here, we aim to explore DFC across a spectrum of symptomatically-related disorders including bipolar disorder with psychosis (BPP), schizoaffective disorder (SAD), and schizophrenia (SZ). We introduce a group information guided independent component analysis procedure to estimate both group-level and subject-specific connectivity states from DFC. Using resting-state fMRI data of 238 healthy controls (HCs), 140 BPP, 132 SAD, and 113 SZ patients, we identified measures differentiating groups from the whole-brain DFC and traditional static functional connectivity (SFC), separately. Results show that DFC provided more informative measures than SFC. Diagnosis-related connectivity states were evident using DFC analysis. For the dominant state consistent across groups, we found 22 instances of hypoconnectivity (with decreasing trends from HC to BPP to SAD to SZ) mainly involving post-central, frontal, and cerebellar cortices as well as 34 examples of hyperconnectivity (with increasing trends HC through SZ) primarily involving thalamus and temporal cortices. Hypoconnectivities/hyperconnectivities also showed negative/positive correlations, respectively, with clinical symptom scores. Specifically, hypoconnectivities linking postcentral and frontal gyri were significantly negatively correlated with the PANSS positive/negative scores. For frontal connectivities, BPP resembled HC while SAD and SZ were more similar. Three connectivities involving the left cerebellar crus differentiated SZ from other groups and one connection linking frontal and fusiform cortices showed a SAD-unique change. In summary, our method is promising for assessing DFC and may yield imaging biomarkers for quantifying the dimension of psychosis. Hum Brain Mapp 38:2683-2708, 2017. © 2017 Wiley Periodicals, Inc.

Keywords: bipolar disorder; dynamic functional connectivity; functional magnetic resonance imaging; independent component analysis; schizoaffective disorder; schizophrenia.

Figures

Fig. 1
Fig. 1
Framework for the DFC analyses. (A) Estimation of dynamic connectivity using a sliding time window method. (B) The first step of GIG-ICA. For each group, the window-direction concatenated dynamic connectivity of all subjects was decomposed by one ICA to obtain the GSs and the associated SFs. (C) The second step of GIG-ICA. Based on the dominant GS and the individual-subject dynamic connectivity, we used a multiple-objective optimization to estimate the dominant SS for each subject.
Fig. 2
Fig. 2
(A) The whole-brain dynamic connectivity of one HC from Hartford site. Top panel: The window-direction concatenated dynamic connectivity. Each column represents the connectivity strengths of all ROI pairs at one window, and each row represents the dynamics of connectivity strengths of one pair of ROIs. Middle panel: The connectivity matrices at three time windows marked by arrows in the top panel. Bottom panel: Mean of similarity (measured by correlation) between any two connectivity matrices in two windows with a specific distance. (B) The connectivity matrix from the SFC analyses of the same subject. The x-axis and y-axis in the bottom panel of (A) and (B) denotes the ROI ID, which corresponds to brain regions from the AAL template (see supplementary Table S2).
Fig. 3
Fig. 3
Reliability of GSs obtained from ICASSO runs. (A) ICASSO results of the group-level states. Clusters are indicated by red convex hulls and white/red lines connect similar estimates. The cyanic circles indicate the reliable GSs, which were used for consequent analyses. (B) Similarity matrix among the states from original and additional 100 ICASSO runs. Each similarity matrix was computed based on 5×101 states obtained from original and additional 100 ICASSO runs. Each block on the diagonal of one similarity matrix reflects the similarity among corresponding states computed from 101 ICASSO runs.
Fig. 4
Fig. 4
The matched GSs of HC, BPP, SAD and SZ groups and their correlation matrix. Each row of the first four rows includes the connectivity matrices of GSs for one group. Contribution of each GS to dynamic connectivity is shown along with the GS matrix. Each matrix in the last row shows the correlation matrix of the matched GSs from four groups. The similarity measure reflects the mean of those correlations. The first column corresponds to the dominant GS.
Fig. 5
Fig. 5
The visualized connectivity patterns of the matched GSs for HC, BPP, SAD and SZ groups. The connectivity patterns are shown using the same sparsity, and the red and blue lines denote positive and negative values in the GS matrix (shown in Fig. 4), respectively.
Fig. 6
Fig. 6
Reliability of GSs obtained from different permutations. (A) Similarity matrix of GSs from 100 permutations for each group. Each block on the diagonal of one similarity matrix reflects the similarity among corresponding states computed from 100 permutations. (B) Projection of the estimated GSs from 100 permutations and original subjects for each group. Corresponding GSs from different permutations are shown using dots with the same color. Each GS calculated from the original subjects is shown by a “+”. (C) Mean state of the corresponding GSs from 100 permutations for each group. The correlation between each mean GS and its associated GS from the original subjects is shown in the title of each subfigure. State i (i=15) corresponds to GS i (i=15) in Fig. 4.
Fig. 7
Fig. 7
(A) Values of SFs in the concatenated windows of all subjects for each state. (B) The percentage of the positively and negatively active windows of each state. The percentages from different subjects in the same group are shown using a boxplot. For each boxplot, the central line is the median; the square is the mean; and the edges of the box are the 25th and 75th percentiles. The whiskers extend to 1 inter-quartile range, and the outliers are displayed with a “*” sign. Any pair of groups with significant group difference tested by two-sample t-tests (p < 0.05 with Bonferroni correction) is denoted by a line. For State 4 of SZ and State 5 of SAD, we don’t display their comparison results with the associated states from other groups, due to that they showed unique connectivity patterns. State i (i=15) corresponds to GS i (i=15) in Fig. 4.
Fig. 8
Fig. 8
(A) Statistical values log10(p), which were identified by performing ANCOVA on each FC’s strengths in the dominant SSs of the four groups. (B) Partial eta squared (reflecting effect size) of each FC in the dominant SS, tested by ANCOVA. (C) The visualization of the 166 discriminative FCs (p < 0.01 with Bonferroni correction). (D) 22 FCs which showed decreasing trends in the dominant SS from HC to BPP to SAD to SZ, measured by the mean connectivity strength. (E) 34 FCs which had increasing trends in the dominant SS from HC to BPP to SAD to SZ, measured by the mean connectivity strength. (F) 14 FCs which showed significant difference in HC vs. SAD, HC vs. SZ, BPP vs. SAD, and BPP vs. SZ, tested by two-sample t-tests (p < 0.01 with Bonferroni correction). In (C)-(F), the thickness of each line reflects the associated F-value in ANCOVA.
Fig. 9
Fig. 9
Statistical analyses and symptom association results of five hypoconnectivities that had significant correlations with the symptom scores. Statistical analyses result of each FC linking two ROIs is shown using a subfigure, where each bar shows the mean of connectivity strengths across subjects in one group, and the title includes the p-value of ANCOVA. Any pair of groups with significant difference (two-sample t-test, p < 0.01 with Bonferroni correction) is denoted using two symbols with the same color and shape. Significant association was identified by computing Pearson correlation between the strengths of each discriminative FC and the symptom scores of patients (p < 0.05 with Bonferroni correction). The following similar figures are shown using the same manner.
Fig. 10
Fig. 10
Statistical analyses and symptom association results of 12 hyperconnectivities that showed significant correlations with the symptom scores.
Fig. 11
Fig. 11
Statistical analyses results of 14 FCs showing significant differences in HC vs. SAD, HC vs. SZ, BPP vs. SAD, and BPP vs. SZ, assessed by two-sample t-tests (p < 0.01 with Bonferroni correction).
Fig. 12
Fig. 12
(A) Statistical analyses results of three FCs that showed significant group differences between the SZ group and the other three groups, tested by two-sample t-tests (p < 0.01 with Bonferroni correction). Last sub-figure shows the significant association with the symptom scores (p < 0.05 with Bonferroni correction). (B) Statistical analyses result of one FC that showed significant group difference between the SAD group and the other three groups.
Fig. 13
Fig. 13
(A) P-value map obtained from performing ANCOVA on each FC’s strengths in the dominant subject-specific states of the original four groups. (B) All FCs’ associated p-values (i.e., the frequencies or tail probabilities) that were computed based on ANCOVA results of the dominant state from 1000 permutations. (C) P-values (i.e., the frequencies or tail probabilities) obtained from the permutation test of the 166 discriminative FCs (representing the significant group differences among the original four groups).
Fig. 14
Fig. 14
The mean static FC matrix across subjects and its visualized pattern for HC, BPP, SAD and SZ group, respectively. The red and blue lines represent positive and negative connectivity strengths, respectively.
Fig. 15
Fig. 15
(A) Statistical values log10(p), which were identified by performing ANCOVA on each FC’s strengths in the static FC matrix of the four groups. (B) Partial eta squared of each FC in the SFC matrix, examined by ANCOVA. (C) The visualization of the 29 discriminative FCs (p < 0.01 with Bonferroni correction). (D) Six FCs that showed decreasing trends from HC to BPP to SAD to SZ using the static connectivity analyses, measured by the mean connectivity strength. (E) Three FCs showing increasing trends across the four groups using the static connectivity analyses, measured by the mean connectivity strength. In (C)-(E), the thickness of each line reflects the associated F-value in ANCOVA.
Fig. 16
Fig. 16
(A) Statistical analyses results of all six hypoconnectivities in the SFC analyses. (B) Statistical analyses results of all three hyperconnectivities in the SFC analyses. (C) Significant associations between FC strengths and the symptom scores of patients (p < 0.05 with Bonferroni correction). The connectivity strengths were Fisher’s r-to-z transformed.

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