Elsevier

Biological Psychiatry

Volume 71, Issue 10, 15 May 2012, Pages 881-889
Biological Psychiatry

Archival Report
Differences in Resting-State Functional Magnetic Resonance Imaging Functional Network Connectivity Between Schizophrenia and Psychotic Bipolar Probands and Their Unaffected First-Degree Relatives

https://doi.org/10.1016/j.biopsych.2012.01.025Get rights and content

Background

Schizophrenia and bipolar disorder share overlapping symptoms and genetic etiology. Functional brain dysconnectivity is seen in both disorders.

Methods

We compared 70 schizophrenia and 64 psychotic bipolar probands, their respective unaffected first-degree relatives (n = 70, and n = 52), and 118 healthy subjects, all group age-, gender-, and ethnicity-matched. We used functional network connectivity analysis to measure differential connectivity among 16 functional magnetic resonance imaging resting state networks. First, we examined connectivity differences between probands and control subjects. Next, we probed these dysfunctional connections in relatives for potential endophenotypes. Network connectivity was then correlated with Positive and Negative Syndrome Scale (PANSS) scores to reveal clinical relationships.

Results

Three different network pairs were differentially connected in probands (false-discovery rate corrected q < .05) involving five individual resting-state networks: (A) fronto/occipital, (B) anterior default mode/prefrontal, (C) meso/paralimbic, (D) fronto-temporal/paralimbic, and (E) sensory-motor. One abnormal pair was unique to schizophrenia, (C-E), one unique to bipolar, (C-D), and one (A-B) was shared. Two of these three combinations (A-B, C-E) were also abnormal in bipolar relatives but none was normal in schizophrenia relatives (nonsignificant trend for C-E). The paralimbic circuit (C-D), which uniquely distinguished bipolar probands, contained multiple mood-relevant regions. Network relationship C-D correlated significantly with PANSS negative scores in bipolar probands, and A-B with PANSS positive and general scores in schizophrenia.

Conclusions

Schizophrenia and psychotic bipolar probands share several abnormal resting state network connections, but there are also unique neural network underpinnings between disorders. We identified specific connections that might also be candidate psychosis endophenotypes.

Section snippets

Subjects

We assessed 118 normal control subjects, 70 first-degree relatives of SZ, 52 BP first-degree relatives, 64 psychotic BP, and 70 SZ patients (age-, gender-, and ethnicity-matched to control subjects) (Table 1). The DSM-IV (50) consensus diagnoses were established by trained clinical raters and senior diagnosticians with all clinical data and Structured Clinical Interviews for DSM Diagnoses (51) interviews: inter-rater reliability was >.90 among raters. Probands were clinically stable with

Results

The ICA identified 16 independent RSNs of interest, yielding 120 different pair-wise network connections/subject. A detailed region list (with cluster spans) within each significantly modulated functional network is provided in Table S1 and Figure S1 in Supplement 1. Icasso determined that all networks were highly stable (Reliability Index > .95).

Discussion

We sought to delineate common and unique FNC abnormalities in SZ and psychotic BP and to determine which of these were detectable in the unaffected relatives of these probands, thereby examining their possible genetic origin. With a combination of ICA and a validated lagged correlation FNC technique on component time-course data, we extracted spontaneous low-frequency resting state hemodynamic fluctuations in 16 independent brain circuits, all of which resembled previously identified networks

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