Comparison of Resting-State Spontaneous Brain 1 Activity between Treatment-Naive Patients with 2 Schizophrenia and Obsessive-Compulsive Disorder 3

Department of Psychiatry, the Affiliated Wuxi Mental Health Center of Nanjing 6 Medical University, Wuxi, Jiangsu 214151, People’s Republic of China; 7 School of Mental Health and Psychological Sciences, Anhui Medical University, Hefei, 8 Anhui 230032, People’s Republic of China; 9 Anhui Province Key Laboratory of Cognition and Neuropsychiatric Disorders & 10 Collaborative Innovation Center of Neuropsychiatric Disorders and Mental Health, 11 Hefei, Anhui 230032, People’s Republic of China; 12 Department of Medical imaging, East China Sanatorium, Wuxi, Jiangsu 214100, 13 People’s Republic of China; 14 These authors contributed equally to this work. 15

2 Background: Schizophrenia (SZ) and Obsessive-compulsive disorder (OCD) share 19 many demographic and clinical symptoms, genetic risk factors, pathophysiological 20 underpinnings, and brain structure and function. However, the differences in the 21 spontaneous brain activity patterns between the two diseases remain unclear. Here this 22 study aimed to compare the features of intrinsic brain activity in treatment-naive 23 patients with schizophrenia (SZ) and obsessive-compulsive disorder (OCD) and to 24 explore the relationship between spontaneous brain activity and the severity of

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There is increasing evidence that SZ and OCD share neurobiological abnormalities [4, 60 5], but some studies have failed to find overlap between them. For example, some 61 studies have found that compared with OCD, SZ represents more serious biological 62 disorders and greater neurological abnormalities [6][7][8]. It was found that there were 63 abnormalities in brain structure and brain function activity in patients with SZ and OCD 64 compared to those in healthy controls [9][10][11][12]. However, the unique and shared 65 neuroanatomical characteristics of the two diseases have not been fully identified [2, 5, 66 8]. To address the issue, there have been increasing research proposals to directly 67 compare the brain imaging characteristics between SZ and OCD under the same 68 5 research methodology and framework, which is conducive to a better understanding of 69 the relationship between the two disorders [5,8,13,14]. 70 Resting-state functional magnetic resonance imaging (rs-fMRI) is a promising tool 71 for examining the blood oxygen level-dependent (BOLD) signal of the spontaneous 72 fluctuation of the whole brain, which does not require subjects to participate in 73 cognitive activities and is more convenient in clinical practice [15,16]. Several methods 74 such as the amplitude of low-frequency fluctuation (ALFF), regional homogeneity 75 (ReHo), and degree centrality (DC) have been proposed to explore spontaneous brain 76 activity in local and distant brain regions. These three values complement each other 77 and define brain functional characteristics from different perspectives [17][18][19][20][21][22][23]. ALFF is 78 an indicator that is used to detect the regional intensity of spontaneous fluctuation in 79 the BOLD signal, which pinpoints the spontaneous neural activity of a specific region 80 and physiological state of the brain in a resting state [17,19]. The ReHo method, testing 81 the local correlations in BOLD time series by using Kendall's coefficient of 82 concordance (KCC), is often used to investigate regional synchronizations of temporal 83 changes in the brain. A higher ReHo value for a given brain region indicates greater 84 regional coherence [20,21] Therefore, in the present study, we aimed to compare the characteristics of resting-state 123 spontaneous brain activity between treatment-naive patients with SZ and OCD by 124 adopting ALFF, ReHo and DC, and further to explore the relationships between brain 125 spontaneous activities and clinical symptoms. We hypothesized that both SZ and OCD 126 have abnormal spontaneous neural activity, whereas they share distinct neural activity.

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This study was to compare the SZ mechanism with the OCD mechanism in ALFF,    Medical System, Erlangen, Germany) and a 12-channel phased-array head coil. All 160 participants, whose heads were fixed with foam pads to reduce scanner noise and head 161 motion, were required to close their eyes, to relax their minds but not to fall asleep, and 162 to move as little as possible during imaging acquisition. Three-dimensional T1-163 weighted images were acquired using the 3D magnetization-prepared rapid acquisition  average ± standard deviation, while the non-normal distribution data were presented by 228 the median (the first quartile-the third quartile). The age and education level of the three 229 groups showed normal distribution, and then a one-way analysis of variance (ANOVA) 230 was used to test differences among the three groups. The course of disease of the two 231 patient groups was not subject to the normal distribution, and a Mann-Whitney U test 232 was used to assess between-group differences. The mean framewise displacement (FD) 233 was also not subject to the normal distribution, and a Kruskal-Wallis test was used to 234 detect whether there were significant differences among the three groups. A P value of 235 < 0.05 was considered to be statistically significant.

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The statistical analysis of fMRI data was conducted using SPM12 software. The  Table 1. There was no significant difference in the mean FD among SZ group, OCD 256 group and HC group (P > 0.05). There were significant differences in age and education 257 level among the three groups (P < 0.05), but not in sex (P > 0.05). The results of post-258 hoc analysis showed that the age of OCD group was lower than that of HC group, and 259 the education level of SZ group was lower than that of HC group (Bonferroni, P < 0.05).

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The duration of disease in OCD group was significantly longer than that in SZ group 261 (P < 0.05).