TY - JOUR
T1 - Differences in resting-state functional magnetic resonance imaging functional network connectivity between schizophrenia and psychotic bipolar probands and their unaffected first-degree relatives
AU - Meda, Shashwath A.
AU - Gill, Adrienne
AU - Stevens, Michael C.
AU - Lorenzoni, Raymond P.
AU - Glahn, David C.
AU - Calhoun, Vince D.
AU - Sweeney, John A.
AU - Tamminga, Carol A.
AU - Keshavan, Matcheri S.
AU - Thaker, Gunvant
AU - Pearlson, Godfrey D.
N1 - Funding Information:
This study was funded by National Institute of Mental Health (NIMH) Grants R37MH43375 and R01MH074797 (GP), the von Humboldt Foundation and NIMH MH077862 (JS), National Institutes of Health/National Institute of Biomedical Imaging and Bioengineering : 2R01 EB000840 and National Institutes of Health/National Center for Research Resources : 5P20RR021938 (VDC), NIMH MH 78113 (MSK), NIMH MH077851 (CAT), NIMH 5R01 MH077945-03 (GT, CAT, GP, MK, JS). Results were presented at the 65th Annual Meeting of the Society of Biological Psychiatry; May 20-22, 2010; New Orleans, Louisiana.
Funding Information:
John Sweeney is a consultant to Pfizer, Asubio, Takeda, and Lilly and has a research grant from Janssen . Carol Tamminga has served as ad hoc consultant ($10,000) for Intracellular Therapies; organizer/unpaid volunteer for the International Congress on Schizophrenia Research; council member/unpaid volunteer for the National Alliance on Mental Illness; and as Deputy Editor (>$10,000) for the American Psychiatric Association. All other authors reported no biomedical financial interests or potential conflicts of interest.
PY - 2012/5/15
Y1 - 2012/5/15
N2 - 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.
AB - 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.
KW - Bipolar
KW - default mode
KW - functional connectivity
KW - gene
KW - relatives
KW - resting state
KW - schizophrenia
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U2 - 10.1016/j.biopsych.2012.01.025
DO - 10.1016/j.biopsych.2012.01.025
M3 - Article
C2 - 22401986
AN - SCOPUS:84860137822
SN - 0006-3223
VL - 71
SP - 881
EP - 889
JO - Biological Psychiatry
JF - Biological Psychiatry
IS - 10
ER -