Wenjing Zhang1, Du Lei1, Brett Clementz2, Carol Tamminga3, Matcheri Keshavan4, Sarah Keedy5, Godfrey Pearlson6, Elliot Gershon5, Jeffrey Bishop7, Jieke Liu1, Qiyong Gong1, John Sweeney8, and Su Lui1
1Huaxi MR Research Center (HMRRC), Department of Radiology, West China Hospital of Sichuan University, Chengdu, China, 2Department of Psychology, University of Georgia, Athens, GA, United States, 3Department of Psychiatry, University of Texas Southwestern Medical Center, Dallas, TX, United States, 4Department of Psychiatry, Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, MA, United States, 5Department of Psychiatry and Behavioral Neuroscience, University of Chicago, Chicago, IL, United States, 6Department of Psychiatry, School of Medicine, Yale University, New Haven, CT, United States, 7Department of Experimental and Clinical Pharmacology, College of Pharmacy, University of Minnesota, Minneapolis, MN, United States, 8Department of Psychiatry, University of Cincinnati, Cincinnati, OH, United States
Synopsis
Recently, new approaches have been developed
using graph theory to identify deficits in gray matter networks at individual
level. In the current study, by investigating single-subject graphs based on
gray matter morphology to define neuroanatomic networks in a large group of
individuals across psychotic disorders (n=330), we observed disrupted network
organizations associated with superior temporal and prefrontal regions within
the gray matter networks in patients, which were also negatively associated
with severity of psychotic symptoms. These findings showed the utility of graph
theory based measures of neuroanatomic network organization to extend our understanding
of the neurobiology underlying psychotic disorders.
INTRODUCTION
Recent studies have identified
discrete clusters of patients across the spectrum of psychotic disorders with
defining characteristics including features of gray and white matter, 1 regional brain activity 2 and
resting-state connectivity.3 Many of these studies focused on regional brain deficits
while the neuropathology of psychotic disorders is believed
to be more closely to abnormal network organization. 4 Previous studies
have typically characterized network organization via fMRI measures or
write-matter connectivity measures. However, the functional changes depend greatly
on the brain states whereas the white-matter measures could be easily affected
by tractography algorithms and the measures used for the graph construction. Recently, new
approaches have been developed using graph theory to identify deficits
in gray matter networks that have now been developed for use in individual
patients.5 In the present study, we aimed to demonstrate the
feasibility and utility of this new approach for identifying atypical gray
matter network alterations in a large cohort of patients with serious mental
illness.METHODS
N=854 total participants
from the B-SNIP-1 consortium were included in this study, including 330
psychosis probands (109 with schizophrenia, 88 with schizoaffective disorder, and
133 with psychotic bipolar disorder), plus 320 of their nonpsychotic first
degree relatives and 204 healthy controls. Detailed descriptions of how the
B-SNIP clinical population was enrolled and clinically assessed are available. 6
We followed the methodology proposed by Tijms et al 5 to extract individual structural
morphology brain networks, and extended it according to Batalle et
al 7 to normalize networks to
a common comparable framework based on the AAL parcellation template. In gray
matter graphs, nodes represent small cortical areas whereas edges represent
statistical similarities in regional gray matter morphology between nodal
regions. GRETNA toolbox (www.nitrc.org/projects/gretna/) was used to calculate the global
and nodal network topological properties of individual brain networks. In
statistical analysis, we identified differences in network organization between
patients, their nonpsychotic relatives and controls with age, sex, race and handedness included as
covariates. Altered structural network metrics in probands were also correlated with symptom
severity.RESULTS
All probands, their
nonpsychotic relatives and healthy controls showed small-world architectures
(i.e., σ > 1) at all connection densities. There were no significant
differences among the three participant groups in global network properties. However,
significant group differences were found in regional network organization. Relative
to both healthy controls and nonpsychotic relatives, probands showed decreased
nodal degree in right superior frontal gyrus (SFG) and bilateral superior temporal
gyrus (STG), and lower nodal efficiency in bilateral STG (p<0.05, Bonferroni
corrected). There were no significant differences between nonpsychotic
relatives and healthy controls in any regional network metrics (Figure 1). In probands,
nodal efficiency of left and right STG was negatively associated with severity
of psychotic symptoms as revealed by PANSS scores.DISCUSSION
Compared to functional MRI networks, structural networks analysis may reflect more stable patterns of anatomical organization under conditions, and provide novel information relevant to the neuropathology of brain disorders. The gray matter network
organizations of psychotic probands showed decreased nodal degree and nodal
efficiency mainly in superior temporal gyrus but also in superior prefrontal
cortex. In previous graph theory based analyses using functional connectivity
data, superior temporal regions have also been found to have reduced local
connectivity in psychotic disorders. 8 Deficits of superior temporal cortex may
be clinically relevant because of their potential relevance for psychotic
symptomatology such as auditory hallucinations. 9 This point is consistent with our observation
of an association between PANSS positive symptom scores and decreased nodal efficiency
in superior temporal regions. Thus, our findings show the utility of graph
theory based measures of neuroanatomic network organization for understanding
the neurobiology of psychotic disorders.CONCLUSION
By investigating
single-subject graphs based on gray matter morphology to define neuroanatomic
networks in a large group of individuals across disorders, our findings provide
novel evidence indicating gray matter disorganizations mainly involving the
superior temporal regions that was related to the severity of psychotic
symptomology.Acknowledgements
No acknowledgement found.References
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