Whole-Brain Functional Network Connectivity Abnormalities in Affective and Non-Affective Early Phase Psychosis

Abstract

Psychosis disorders share overlapping symptoms and are characterized by a wide-spread breakdown in functional brain integration. Although neuroimaging studies have identified numerous connectivity abnormalities in affective and non-affective psychoses, whether they have specific or unique connectivity abnormalities, especially within the early stage is still poorly understood. The early phase of psychosis is a critical period with fewer chronic confounds and when treatment intervention may be most effective. In this work, we examined whole-brain functional network connectivity (FNC) from both static and dynamic perspectives in patients with affective psychosis (PAP) or with non-affective psychosis (PnAP) and healthy controls (HCs). A fully automated independent component analysis (ICA) pipeline called "Neuromark" was applied to high-quality functional magnetic resonance imaging (fMRI) data with 113 early-phase psychosis patients (32 PAP and 81 PnAP) and 52 HCs. Relative to the HCs, both psychosis groups showed common abnormalities in static FNC (sFNC) between the thalamus and sensorimotor domain, and between subcortical regions and the cerebellum. PAP had specifically decreased sFNC between the superior temporal gyrus and the paracentral lobule, and between the cerebellum and the middle temporal gyrus/inferior parietal lobule. On the other hand, PnAP showed increased sFNC between the fusiform gyrus and the superior medial frontal gyrus. Dynamic FNC (dFNC) was investigated using a combination of a sliding window approach, clustering analysis, and graph analysis. Three reoccurring brain states were identified, among which both psychosis groups had fewer occurrences in one antagonism state (state 2) and showed decreased network efficiency within an intermediate state (state 1). Compared with HCs and PnAP, PAP also showed a significantly increased number of state transitions, indicating more unstable brain connections in affective psychosis. We further found that the identified connectivity features were associated with the overall positive and negative syndrome scale, an assessment instrument for general psychopathology and positive symptoms. Our findings support the view that subcortical-cortical information processing is disrupted within five years of the initial onset of psychosis and provide new evidence that abnormalities in both static and dynamic connectivity consist of shared and unique features for the early affective and non-affective psychoses.

Keywords: affective psychosis; dynamic functional network connectivity (dFNC); early phase psychosis; functional network connectivity (FNC); graphic measure; non-affective psychosis.

FIGURE 1

Flowchart of capturing whole-brain connectivity features. (A) group ICA is performed on two independent healthy controls datasets and the estimated independent components (ICs) are matched by the spatial correlation. Matched ICs are identified as intrinsic connectivity networks (ICNs) according to their spatial maps and the ICNs are used as spatial templates to calculate components for the HCPEP data. (B) Pearson correlation coefficients are calculated using the TCs across the whole scans. (C) A sliding window approach is used to estimate dFNC. K means clustering is performed on the dFNC estimates. State occurrences and transitions are calculated, and graphic measure is calculated for each state.

FIGURE 2

Spatial maps of the identified ICNs and sFNC matrix. (A) 53 ICNs were identified and sorted into seven resting-state functional domains. Each color represents a single ICN. (B) Averaged sFNC matrix across participants. (C) The functional connectivity profile of the averaged sFNC matrix.

FIGURE 3

Findings of sFNC analysis. (A) N-way ANOVA results and pair-wise group comparisons (HC vs. PnAP, HC vs. PAP, PnAP vs. PAP). The FNC showed significant difference in the ANOVA test are marked in red. For pair-wise comparison results, significantly different FNC are marked in red or blue to indicate the direction of difference (e.g., HC > PnAP or PnAP > HC). The Functional connectivity profile of the significant FNC is displayed by circular graph. (B) Exemplars of abnormal sFNC between groups. Data are presented as mean values ± standard error of mean (SEM). PnAP and PAP showed shared and unique abnormalities. (C) Correlations between abnormal sFNC and PANSS total score. ^∗Significance p < 0.05, FDR corrected.

FIGURE 4

Group differences in dynamic characteristics of dFNC states. (A) Functional connectivity profile of each brain state: top 200 FNC with connectivity strength >0.2 in each state, representing the strongest functional-relationships between brain regions. (B) Group differences in the fractional rate and state transitions. Bars represent the mean of occurrences and error bars represent the SEM. (C) Scatter plots of correlations between dynamic characteristics and PANSS total score. ^∗Significance p < 0.05, FDR corrected.

FIGURE 5

Local Efficiency of dFNC states. (A) Comparisons of averaged local efficiency among brain states. (B) Functional connectivity profile of state 1 and its connectivity mapping to the ICBM152 brain template. (C) Group differences in local efficiency of ICNs within state 1. Bars represent the mean of occurrences and error bars represent the SEM. ^∗Significance p < 0.05, FDR corrected.