Akiko Uematsu1,2, Hidenori Yamasue3, Kiyoto Kasai4, and Shinsuke Koike4,5
1Graduate School of Arts and Sciences, University of Tokyo, Tokyo, Japan, 2RIKEN CBS, Saitama, Japan, 3Departmentof Psychiatry, Hamamatsu University School of Medicine, Hamamatsu, Japan, 4The University of Tokyo Hospital, Tokyo, Japan, 5Center for Evolutionary Cognitive Science, University of Tokyo, Tokyo, Japan
Synopsis
In this study, we examined structural brain deterioration in the course
of schizophrenia with multi aspects by utilizing a variety of information derived
from multi-contrast MRI data. Our findings suggested superior
temporal gyrus is the key to understand the onset mechanism of Schizophrenia.
INTRODUCTION
Structural
brain deteriorations, assumably resulting in cognitive and behavioral
dysfunction, in the course of schizophrenia is a well-established finding in
MRI studies1. Understanding the pattern of such progressive
deterioration from prodromal period would provide neurological insight of the
disease mechanism of onset. Nevertheless, few studies have included individuals
at imminent risk of developing schizophrenia. Thus, in this study, we delineated
the differences of brains of patients at risk of mental state (ARMS), first
episode schizophrenia (FEP), chronic schizophrenia (SCZ), and healthy controls
(HC) with multi-contrast MRI data to delineate the deteriorations of both gray
and white matterMETHODS
This
study was approved by the ethics committee of the University of Tokyo Hospital
(No. 397 and 2226). T1-, T2-, diffusion-weighted images were acquired from
a total of 201 individuals (41 SCZ, 26 FES, 27 ARMS, and 116 HC) with 3 Tesla
GE Signa HDx (GE Healthcare, Milwaukee, WI, USA), equipped with a 8 channel
brain phased array coil. These images were acquired with the following parameters:
T1-weighted ; 3D fast-spoiled gradient recalled (3D-FSPGR) sequence with TR /
TE = 6.8/1.94 ms, FA=20°, FOV = 240x240, matrix=256x256, slice thickness=1 mm :
Axial T2-weighted ; fast spin-echo images with parallel imaging technique sequence
with TR / TE = 4400/82.32 ms, lip angle=90°, FOV = 240x240, matrix=256x256,
slice thickness=2.5 mm: Diffusion-weighted; A single-shot spin-echo echo-planar
imaging A single-shot spin-echo echo-planar imaging (EPI) sequence with sequence
with TR / TE = 200000/55.3 ms, flip angle=90°, FOV =
256x256, matrix=256x256, slice thickness=2.0 mm, 30 b1000 directions with 5 b0
s/mm2.
The
details of participants’ criteria in this study was written in our previous
study2, but AMRS were diagnosed according to the Structured Interview
for Prodromal Symptoms (SIPS)3. In addition, FEP patients who have received
antipsychotic medication for less than 16 cumulative weeks were recruited to
minimize the antipsychotic medication on brain. Table 1 gives the demographic
data of the participants in this study.
Adapting HCP pipeline4, we estimated cortical
thickness, area, volume, an T1-weighed/T2-weighted ratio cortical myelin map and
visually and statistically examined gray matter among groups. With diffusion-weighted
data, we calculated not only conventional diffusion tensor metrics but also a
novel methodology, fixel 5 to examine white matter.RESULTS AND DISCUSSIONS
There were various cortical regions that significantly
different from HC group. Especially the thickness of superior temporal gyrus
showed linear thinning as the stage got worse (Figure 1A). The GLM analysis on
cortical structure also showed significant difference in superior temporal
regions (Figure 1B) multiple altered regions different among groups. Nevertheless,
T1-weighted/T2-weighted ratio myelin map did not show any difference among
groups, indicating that the cortical thinning is not due to disruption of
myelinated axons getting into cortical regions. Whereas, fixel-based analysis,
which provides more detailed white matter structural information, demonstrated less
density of assumably arcuate fasciculus and fornix passing on thalamus in left
hemisphere and bilateral minor forceps and superior longitudinal fasciculus in
ARMS, FEP, and SCZ than HC (Figure 2). The mean FA values in these fibers also
statistically supported significant reduction in SCZ (p > 0.01) although
ARMS and FEP groups showed no significance. These results suggested the fiber
bundles gradually teared and reduced its diameter and fiber density as the stage
got worse, reducing its FA values. Since arcuate fasciculus and superior longitudinal
fasciculus stem from or pass through superior temporal gyrus, whose thickness
and volume were significantly smaller in all groups in this study, it could be
said that schizophrenia might have some vulnerability in superior
temporal gyrus cells and their axons connecting between temporal lobe and
parietal/frontal regions.CONCLUSION
To conclude, we examined structural brain deterioration in the course of
schizophrenia with multi aspects by utilizing a variety of information derived
from multi-contrast MRI data. Our findings suggested superior
temporal gyrus is the key to understand the onset mechanism of Schizophrenia.Acknowledgements
No acknowledgement found.References
- Cetin-Karayumak S, Di Biase MA, Chunga N, et al.
White matter abnormalities across the lifespan of schizophrenia: a harmonized
multi-site diffusion MRI study. Mol Psychiatry. 2019.
- Iwashiro, N., Suga, M., Takano, Y., Inoue, H., Natsubori, T.,
Satomura, Y., ... & Gonoi, W. (2012). Localized gray matter volume
reductions in the pars triangularis of the inferior frontal gyrus in
individuals at clinical high-risk for psychosis and first episode for
schizophrenia. Schizophrenia research, 137(1-3),
124-131.
- McGlashan, H., Miller, T. J., & Woods, S. W. (2001).
Structured interview for prodromal syndromes. Version 3.0. PRIME.
- Glasser, M. F., Sotiropoulos, S. N., Wilson, J. A., Coalson,
T. S., Fischl, B., Andersson, J. L., ... & Van Essen, D. C. (2013). The
minimal preprocessing pipelines for the Human Connectome Project. Neuroimage, 80, 105-124.
- Raffelt DA, Tournier JD, Smith RE, et al.
Investigating white matter fibre density and morphology using fixel-based
analysis. Neuroimage. 2017;144:58-73.
doi:10.1016/j.neuroimage.2016.09.029