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BMC Psychiatry

Erschienen in:

Open Access 01.12.2021 | Research

Comparison of resting-state spontaneous brain activity between treatment-naive schizophrenia and obsessive-compulsive disorder

verfasst von: Xiao-Man Yu, Lin-Lin Qiu, Hai-Xia Huang, Xiang Zuo, Zhen-He Zhou, Shuai Wang, Hai-Sheng Liu, Lin Tian

Erschienen in: BMC Psychiatry | Ausgabe 1/2021

Abstract

Background

Schizophrenia (SZ) and obsessive-compulsive disorder (OCD) share many demographic characteristics and severity of clinical symptoms, genetic risk factors, pathophysiological underpinnings, and brain structure and function. However, the differences in the spontaneous brain activity patterns between the two diseases remain unclear. Here this study aimed to compare the features of intrinsic brain activity in treatment-naive participants with SZ and OCD and to explore the relationship between spontaneous brain activity and the severity of symptoms.

Methods

In this study, 22 treatment-naive participants with SZ, 27 treatment-naive participants with OCD, and sixty healthy controls (HC) underwent a resting-state functional magnetic resonance imaging (fMRI) scan. The amplitude of low-frequency fluctuation (ALFF), regional homogeneity (ReHo) and degree of centrality (DC) were performed to examine the intrinsic brain activity of participants. Additionally, the relationships among spontaneous brain activity, the severity of symptoms, and the duration of illness were explored in SZ and OCD groups.

Results

Compared with SZ group and HC group, participants with OCD had significantly higher ALFF in the right angular gyrus and the left middle frontal gyrus/precentral gyrus and significantly lower ALFF in the left superior temporal gyrus/insula/rolandic operculum and the left postcentral gyrus, while there was no significant difference in ALFF between SZ group and HC group. Compared with HC group, lower ALFF in the right supramarginal gyrus/inferior parietal lobule and lower DC in the right lingual gyrus/calcarine fissure and surrounding cortex of the two patient groups, higher ReHo in OCD group and lower ReHo in SZ group in the right angular gyrus/middle occipital gyrus brain region were documented in the present study. DC in SZ group was significantly higher than that in HC group in the right inferior parietal lobule/angular gyrus, while there were no significant DC differences between OCD group and HC group. In addition, ALFF in the left postcentral gyrus were positively correlated with positive subscale score (r = 0.588, P = 0.013) and general psychopathology subscale score (r = 0.488, P = 0.047) respectively on the Positive and Negative Syndrome Scale (PANSS) in SZ group. ALFF in the left superior temporal gyrus/insula/rolandic operculum of participants with OCD were positively correlated with compulsion subscale score (r = 0.463, P = 0.030) on the Yale-Brown Obsessive-Compulsive Scale (Y-BOCS). The longer the illness duration in SZ group, the smaller the ALFF of the left superior temporal gyrus/insula/rolandic operculum (Rho = 0.-492, P = 0.020). The longer the illness duration in OCD group, the higher the ALFF of the right supramarginal gyrus/inferior parietal lobule (Rho = 0.392, P = 0.043) and the left postcentral gyrus (Rho = 0.385, P = 0.048), and the lower the DC of the right inferior parietal lobule/angular gyrus (Rho = − 0.518, P = 0.006).

Conclusion

SZ and OCD show some similarities in spontaneous brain activity in parietal and occipital lobes, but exhibit different patterns of spontaneous brain activity in frontal, temporal, parietal, occipital, and insula brain regions, which might imply different underlying neurobiological mechanisms in the two diseases. Compared with OCD, SZ implicates more significant abnormalities in the functional connections among brain regions.

Xiao-Man Yu and Lin-Lin Qiu contributed equally to this work.

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schizophrenia

OCD

obsessive-compulsive disorder

fMRI

resting-state functional magnetic resonance imaging

ALFF

amplitude of low-frequency fluctuation

ReHo

regional homogeneity

degree of centrality

healthy controls group

PANSS

Positive and Negative Syndrome Scale

Y-BOCS

Yale-Brown Obsessive-Compulsive Scale

rs-fMRI

resting-state functional magnetic resonance imaging

BOLD

blood oxygen level-dependent

KCC

Kendall’s coefficient of concordance

functional connectivity

CSTC

cortico-striato-thalamo-cortical

dlPFC

dorsolateral prefrontal cortex

IOG

inferior occipital gyrus

OFC

orbitofrontal cortex

HARS

Hamilton Rating Scale for Anxiety

24-HDRS

24-item Hamilton Rating Scale for Depression

time repetition

time echo

FOV

field of view

MNI

montreal neurological institute

FWHM

full-width half-maximum

mean FD

Jenkinson’s mean framewise displacement

FEW

family-wise error

ANOVA

one-way analysis of variance

ANCOVA

analysis of covariance

SPSS

statistical package for the social sciences

DMN

default mode network

Background

Although schizophrenia (SZ) and obsessive-compulsive disorder (OCD) are separate diagnostic entities, they both share high comorbidity, and the family history of OCD is a risk factor for SZ, suggesting that they may have some common neurobiological bases [1]. SZ and OCD equally belong to neurodevelopmental disorders and are characterized by similar traits, e.g., reportedly numerous overlaps between the two disorders in some domains, like demographic and clinical characteristics, genetic risk factors, pathophysiological underpinnings, and brain structure and function [2, 3]. There is growing evidence that SZ and OCD share neurobiological abnormalities, while some studies have suggested that SZ is a more serious biological and neurological disorder than OCD, with significant differences in their neural mechanisms [2‐5]. Till now, either shared or the unique neuroanatomical features of these two diseases have not yet been fully identified, consequently calling for a direct comparison of the brain imaging characteristics of SZ and OCD under the same research method and framework, which is more convincing to address this issue, contributing to a better understanding of the relationship between the two diseases [5, 6].

Resting-state functional magnetic resonance imaging (rs-fMRI) is a promising tool for examining the blood oxygen level-dependent (BOLD) signal of the spontaneous fluctuation of the whole brain, which does not require subjects to participate in cognitive activities and is more convenient in clinical practice [7, 8]. In recent years, several methods such as amplitude of low-frequency fluctuation (ALFF), regional homogeneity (ReHo), and degree centrality (DC) have been widely used in the study of spontaneous brain activity in various neuropsychiatric diseases. ALFF is an indicator that is used to detect the regional intensity of spontaneous fluctuation in the BOLD signal, which pinpoints the spontaneous neural activity of a specific region and physiological state of the brain in a resting state [9, 10]. The ReHo method, testing the local correlations in BOLD time series by using Kendall’s coefficient of concordance (KCC), is often used to investigate regional synchronizations of temporal changes in the brain [11, 12]. Based on the voxel level, DC is a measure of the connectome graph indexing the number of direct connections for a given node and reflects its functional connectivity (FC) within the whole brain network without requiring a priori selection [13, 14].

Previous neuroimaging studies have summarized that both SZ and OCD are impaired in several crucial brain regions, including the caudate nucleus, orbitofrontal cortex, anterior cingulate gyrus, and thalamus [15]. Goodkind et al. [16] conducted a meta-analysis of 193 studies based on the voxel-based morphometry (VBM), showing that the dorsal anterior cingulate gyrus and bilateral insula demonstrated consistent reductions in gray matter volume in participants with six different psychiatric disorders, e.g., SZ and OCD, and that lower gray matter in these above-mentioned brain regions was associated with decreased executive function. In addition, although the frontostriatal deficit is involved in the neuropathological mechanisms of SZ and OCD, participants with SZ may exhibit more structural abnormalities and cognitive deficits [17]. Particularly, under the same research conditions, our previous diffusion MRI study showed that SZ and OCD had different patterns of anatomical and topological organizations, which both present more severe and extensive disruptions in SZ [5]. A few studies have used rs-fMRI to directly compare imaging differences between SZ and OCD. Fan et al. [18] found that sustained attention deficits in SZ were significantly correlated with altered FC of the left medial prefrontal cortex (mPFC)-bilateral anterior cingulate cortices and those in OCD were correlated with altered FC of the right mPFC-left superior frontal gyrus. Wang et al. [19] compared the strength of FC between 19 subregions of default mode network (DMN) and whole-brain voxels in SZ group, OCD group, and schizo-obsessive comorbidity (SOC) group, respectively, and found that the FC between the subregions of DMN and executive control network (ECN) increased in all three patient groups compared with the healthy control group. The difference is that both the SZ and SOC groups showed increased FC between the middle temporal gyrus and the subregions of the DMN, where, however, the OCD group exhibited decreased FC. Several previous studies have used ALFF, ReHo, and DC methods to find that both SZ and OCD demonstrate abnormalities in spontaneous brain activity, which may have both diffuse and regionally-specific characteristics [20, 21]. Taken together, associations of brain networks between participants with SZ and OCD have been proposed and many previous fMRI studies have demonstrated abnormalities of ALFF, ReHo, and DC in multiple brain regions of participants with either SZ or OCD, whereas no consistent conclusion has been reached [20, 21].

However, as far as we know, there have been very few studies directly compare intrinsic brain abnormalities between SZ and OCD based on the same research conditions and framework. Therefore, in the present study, we aimed to compare the characteristics of resting-state spontaneous brain activity between treatment-naive participants with SZ and OCD by adopting ALFF, ReHo and DC, and further to explore the relationships among brain spontaneous activities, the severity of clinical symptoms, and the duration of illness. We hypothesized that both SZ and OCD have abnormal spontaneous neural activity, whereas they share distinct neural activity.

Methods

Participants

This study was approved by the Research Ethics Review Board of Wuxi Mental Health Center, and all participants provided written informed consent. We recruited 29 SZ and 29 OCD subjects from Wuxi Mental Health Center, Nanjing Medical University, China, as well as 65 healthy controls (HC) from the local community. All subjects met the DSM-IV-TR [22] criteria and none of them had received any pharmacologic treatment or psychotherapies before the MRI scanning of this study. MRI scans and evaluations of the severity of clinical symptoms of the participants were completed on the same day. The positive and Negative Syndrome Scale (PANSS) [23] was conducted in SZ by experienced psychiatrists. As for OCD, the severity of OCD symptoms, anxious and depressive symptoms were respectively assessed by Yale-Brown Obsessive-Compulsive Scale (Y-BOCS) [24], Hamilton Rating Scale for Anxiety(HARS) [25], and 24-item Hamilton Rating Scale for Depression (24-HDRS) [26], respectively. Individuals having a lack of current or historic diagnoses of any psychiatric disorder were chosen as healthy controls. Besides, individuals with family histories of any psychiatric disorders or neurological illnesses were excluded from healthy controls. All recruited participants were right-handed when assessed with Edinburgh Handedness Inventory [27]. Exclusion criteria for all participants included brain injury, intracranial pathology, neurological illness, alcohol, nicotine or other substances abuse or dependence, pregnancy, contraindications of MRI, and head movements during scanning more than 3 mm or 3.0° in any direction. In data preprocessing, 7 SZ, 2 OCD and 5 HC were excluded due to incomplete functional imaging or excessive head movement. Finally, 22 SZ, 27 OCD and 60 HC were included in the statistical analyses. Table 1 provides detailed demographic and clinical characteristics.

Table 1

Demographic, clinical and head-motion characteristics of the participants in this study

Variables	SZ (n = 22)	OCD (n = 27)	HC (n = 60)	Statistics (F/ χ²/ T/Z/H)	P value	P1–2	P1–3	P2–3
Age (years)	33.41 ± 11.03 (16–61)	26.89 ± 8.15 (16–43)	32.87 ± 10.78^a (17–50)	3.65	0.029^*c	0.087	1.000	0.040
Education (years)	10.77 ± 4.74	13.26 ± 2.96	14.02 ± 3.72	5.94	0.004^*d	0.072	0.002	1.000
Sex (M/F)	11/11	21/6	38/22	4.12	0.128^#	–	–	–
Disease duration (years)	1.29 (0.17,3.25)	3.00 (1.00, 6.00) ^b	–	−2.61	0.009^&	–	–	–
PANSS positive score	27.18 ± 4.63	–	–	–	–	–	–	–
PANSS negative score	19.82 ± 5.14	–	–	–	–	–	–	–
PANSS general score	46.50 ± 7.58	–	–	–	–	–	–	–
PANSS total score	93.50 ± 12.39	–	–	–	–	–	–	–
Y-BOCS score						–	–	–
Obsession score	–	12.33 ± 3.87	–	–	–	–	–	–
Compulsive score	–	8.70 ± 2.95	–	–	–	–	–	–
Total score	–	21.04 ± 5.93	–	–	–	–	–	–
HARS score	–	14.00 (12.00,19.00)	–	–	–	–	–	–
24-HDRS score	–	16.30 ± 7.83	–	–	–	–	–	–
Mean FD	0.07 (0.05,0.10)	0.08 (0.05,0.12)	0.07 (0.06,0.12)	0.95	0.623^∇	–	–	–

Note: ^a Values are presented as mean ± SD

^b Values are presented as median (first quartile, third quartile)

^*, one-way ANOVA; ^#, x² test; ^&,Mann–Whitney U test; ^∇,Kruskal–Wallis test

^c post hoc analysis showed that participants with OCD differed significantly from controls (Bonferroni, P < 0.05)

^d post hoc analysis showed that participants with SZ differed significantly from controls (Bonferroni, P < 0.05)

P < 0.05 is considered significant

P1–2 for SZ group versus OCD group, P1–3 for SZ group versus HC group, P2–3 for OCD group versus HC group

Abbreviations: HC, healthy controls; OCD, participants with obsessive-compulsive disorder; SZ, participants with schizophrenia; PANSS, Positive and Negative Syndrome Scale; HARS, the Hamilton Rating Scale for Anxiety; 24-HDRS, the 24-item Hamilton Rating Scale for Depression; Y-BOCS, the Yale-Brown Obsessive-Compulsive Scale; Mean FD, mean framewise displacement

Data acquisition

MRI was performed at the Department of Medical Imaging, Wuxi People’s Hospital, Nanjing Medical University by using a 3.0-Tesla Magnetom Trio Tim (Siemens Medical System, Erlangen, Germany) and a 12-channel phased-array head coil. All participants, whose heads were fixed with foam pads to reduce scanner noise and head motion, were required to close their eyes, to relax their minds but not to fall asleep, and to move as little as possible during imaging acquisition. Three-dimensional T₁-weighted images were acquired using the 3D magnetization-prepared rapid acquisition gradient-echo sequence with the following parameters: time repetition (TR) = 2530 m s, time echo (TE) = 3.44 ms, flip angle = 7°, field of view (FOV) = 256 × 256 mm², matrix size = 256 × 256, slice thickness = 1 mm, 192 sagittal slices, acquisition voxel size = 1 × 1 × 1 mm³, total acquisition time = 649 s. After structural MRI scans, the gradient-echo planar imaging sequence was used to obtain the rs-fMRI scans with the following parameters: single shot, TR = 2000 ms, TE = 30 ms, flip angle = 90, FOV = 220 × 220 mm², matrix size = 64 × 64, slice thickness = 4 mm, 33 axial slices, acquisition voxel size = 3.4 × 3.4 × 4 mm³, resulting in 240 volumes.

Data preprocessing

Analysis of the RS-fMRI data was performed by using Data Processing and Analysis of Brain Imaging [28] (DPABI; http://rfmri.org/DPABI_V4.3) in MATLAB 2013b (The Math Works, Natick, MA, USA) based on Statistical Parametric Mapping (SPM12; http://www.fil.ion.ucl.ac.uk/spm/software/spm12). The first 10 time points were discarded for initial signal stabilization. The remaining 230 volumes were corrected for the intra-volume acquisition time delay using slice timing correction and were realigned for head movement correction. If the head movement was more than 3 mm or 3°, the data were excluded from the analysis. To further eliminate the residual effect of motion on rs-fMRI measurement, Jenkinson’s mean framewise displacement (mean FD) was calculated based on their realignment parameters to quantify head motion, which was used as a covariable of all voxel-wise group functional analyses [10, 29]. Each T₁-weighted image was registered with the average functional image, and the image was divided into white matter, gray matter, and cerebrospinal fluid tissue maps. Then the image space was normalized to the standard Montreal Neurological Institute (MNI) space, and the resampling was 3 × 3 × 3 mm³. Subsequently, the generalized linear model was used to regress the signals from white matter and cerebrospinal fluid and the covariates of Friston-24 parameters, and linear trends of the time courses were removed from the fMRI data [30, 31]. Before ALFF analysis, a Gaussian filter (6-mm full-width half-maximum, FWHM) was used for spatial smoothing, but smoothing was performed after ReHo and DC calculations. Smoothing before the calculation of ReHo and DC will cause the regional correlation of adjacent voxels and affect the calculation of the above two parameters, so smoothing was usually carried out after calculation to reduce spatial noise and the incompleteness of the registration effect of the participants [13, 32]. Finally, DPABI_V4.3 was used to calculate ALFF, ReHo and DC.

ALFF analyses

After data preprocessing, the time series for each voxel was transformed to the frequency domain using fast Fourier transforms, and the square root of this spectrum was calculated for each frequency and then averaged across 0.01–0.08 Hz [9]. This averaged square root was used as an ALFF index. For standardization, the ALFF of each voxel was divided by the global mean ALFF, to get the mALFF map [33, 34].

ReHo and DC analyses

ReHo and DC were measured based on unsmoothed data. After preprocessing, a temporal filter (0.01–0.08 Hz) was applied to reduce the influences of high-frequency physiological noises and low-frequency drifts.

The ReHo was obtained on a voxel-by-voxel basis by calculating KCC of a given voxel with those of its 26 nearest neighbors [11]. Then the ReHo of each voxel was divided by the global mean ReHo of each individual, to get the mReHo map [33, 34]. Next, mReHo maps were smoothed with a 6-mm FWHM Gaussian kernel.

After data preprocessing, fMRI data were used to calculate the voxel-wise DC, and then Pearson’s correlation method was utilized to correlate the time series of each voxel with the time series of every other voxel, after which a matrix of Pearson’s correlation coefficients matrix was obtained. Next, the correlation coefficient of r = 0.25 was used as the lowest threshold to eliminate the low time correlation caused by signal noise [13, 35]. Subsequently, the binary DC of the whole-brain network was calculated [34, 36]. As a result, each participant obtained a map of DC of each gray matter voxel. Before group-level statistical analysis, we divided DC of each voxel by the global mean DC, to get the mDC map, and then used Gaussian smoothing kernels (full width half maximum, half-width = 6 mm) to spatially smooth all individual mDC maps [33, 34].

Statistical analyses

The demographic and clinical data of the subjects were analyzed by SPSS 25.0 software (SPSS, Chicago, IL, United States). The normal distribution data were described as the average ± standard deviation, while the non-normal distribution data were presented by the median (the first quartile-the third quartile). The age and education level of the three groups showed normal distribution, and then a one-way analysis of variance (ANOVA) was used to test differences among the three groups. The duration of illness of the two patient groups was not subject to the normal distribution, and a Mann-Whitney U test was used to assess between-group differences. The mean framewise displacement (FD) was also not subject to the normal distribution, and a Kruskal-Wallis test was used to detect whether there were significant differences among the three groups. A P value of < 0.05 was considered to be statistically significant.

In this study, SPM12 software and voxel-wise analysis of covariance (ANCOVA) were used to test the differences in ALFF, ReHo and DC among the three groups. The confounding factors of age, sex, the level of education, and Jenkinson’s mean FD were controlled as covariates. The multiple comparisons correction of statistical F-maps was performed with family-wise error (FWE) cluster-corrected (P < 0.05) when using a primary voxel determining the threshold of P < 0.001 to protect against false-positive findings. For the clusters showing significant differences among the three groups, the mean ALFF, ReHo and DC were extracted from the cluster for each participant. Then the post hoc analyses were conducted using SPSS25.0, and the analyses were corrected for multiple comparisons using Bonferroni correction at a statistical significance level of P < 0.05. Moreover, partial correlation analysis was performed to evaluate the relationship between the ALFF, ReHo and DC extracted from the above-mentioned significant difference clusters respectively and the severity of symptoms (PANSS scores and compulsion subscale scores and obsession subscale scores of Y-BOCS). Age, sex, the level of education, Jenkinson’s mean FD, and duration of disease were taken as covariates. Spearman correlation analysis was conducted with SPSS 25.0 to explore the relationship between ALFF, ReHo, and DC of significantly altered brain regions and the duration of illness of participants of the two patient groups, respectively. Results with P < 0.05 (uncorrected) were considered statistically significant.

Results

Demographics and clinical characteristics

Demographic, clinical variables and the mean FD of the participants are presented in Table 1. The three groups did not differ statistically in the mean FD and sex (P > 0.05). There were significant differences in age and education level among the three groups (P < 0.05). The results of post hoc analyses showed that OCD group was lower in age than HC group, and SZ group was lower in education level than HC group (Bonferroni, P < 0.05). The duration of illness in OCD group was significantly longer than that in SZ group (P < 0.05).

ALFF differences among the three groups

ANCOVA and post hoc analyses were used to compare the differences in ALFF, ReHo and DC among the three groups. As shown in Fig. 1Aand Table 2, significant group differences in ALFF primarily exist in the left superior temporal gyrus/insula/rolandic operculum, left middle frontal gyrus/ precentral gyrus, left postcentral gyrus, right angular gyrus, and right supramarginal gyrus/inferior parietal lobule (voxel significance, P < 0.001; cluster significance, P < 0.05, FWE correction).

Table 2

The ALFF, ReHo and DC clusters with significant between-group differences (Cluster-level P_FWE < 0.05 when voxel-level threshold was P < 0.001)

Feature	Index	Cluster size	Brain regions	side	BA	MNI coordinate			Peak F
Feature	Index	Cluster size	Brain regions	side	BA	x	y	z	Peak F
ALFF	1	47	Angular gyrus	R	39	42	−51	30	29.20
	2	40	Superior temporal gyrus	L	48	−45	−36	24	8.77
			Insula	L	48	−36	−18	15	11.20
			Rolandic operculum	L	48	−36	−27	18	10.85
	3	37	Middle frontal gyrus	L	9	−36	9	51	21.84
			Precentral gyrus	L	6	−36	3	42	12.08
	4	37	Postcentral gyrus	L	4	−24	−30	66	11.39
	5	33	Supramarginal gyrus	R	2	45	−33	39	13.06
			Inferior parietal lobule	R	2	48	−36	51	11.96
ReHo	1	105	Angular gyrus	R	39	42	−51	30	40.14
ReHo			Middle occipital gyrus	R	19	39	−72	33	18.76
DC	1	105	Lingual gyrus	R	18	12	−87	−12	17.18
			Calcarine fissure and surrounding cortex	R	17	6	−78	3	8.32
	2	49	Inferior parietal lobule	R	40	54	−57	48	14.42
			Angular gyrus	R	39	60	−57	30	10.19

Abbreviations: BA, Brodmann area; MNI, Montreal Neurological Institute; R, right; L, left; ALFF, amplitude of low-frequency fluctuation; ReHo, regional homogeneity; DC, degree centrality; P_FWE, P < 0.05, FWE correction

Post hoc t-tests (P < 0.05, Bonferroni correction) showed that compared to HC group, participants with OCD showed higher ALFF in the right angular gyrus (SZ group: 1.08 ± 0.26; OCD group: 1.60 ± 0.48; HC group: 1.17 ± 0.21, Fig. 2A) and the left middle frontal gyrus/precentral gyrus (SZ group: 0.75 ± 0.10; OCD group: 0.92 ± 0.20; HC group: 0.68 ± 0.12, Fig. 2A), and lower ALFF in the left superior temporal gyrus/insula/rolandic operculum (SZ group: 0.76 ± 0.16; OCD group: 0.60 ± 0.11; HC group: 0.75 ± 0.14, Fig. 2A) and the left postcentral gyrus (SZ group: 1.49 ± 0.50; OCD group: 1.00 ± 0.29; HC group: 1.28 ± 0.40, Fig. 2A). Compared to HC group, ALFF in the right supramarginal gyrus/inferior parietal lobule of the two patient groups was lower (SZ group: 1.07 ± 0.18; OCD group: 1.12 ± 0.15; HC group: 1.37 ± 0.29, Fig. 2A). Compared to OCD group, ALFF of SZ group in the left superior temporal gyrus/insula/rolandic operculum and the left postcentral gyrus was significantly higher, while that in the right angular gyrus and the left middle frontal gyrus/precentral gyrus was significantly lower (Fig. 2A).

ReHo differences among the three groups

As shown in Fig. 1Band Table 2, significant group differences in ReHo primarily exist in the right angular gyrus/middle occipital gyrus (voxel significance, P < 0.001; cluster significance, P < 0.05, FWE correction). Compared to HC group, ReHo in the right angular gyrus/middle occipital gyrus (SZ group:1.23 ± 0.20; OCD group:1.47 ± 0.17; HC group: 1.32 ± 0.11, Fig. 2B) was significantly higher in OCD group, whereas that was significantly lower in SZ group (P < 0.05, Bonferroni correction).

DC differences among the three groups

Analyses of ANCOVA showed that there were significant group differences in DC of the right lingual gyrus/calcarine fissure and surrounding cortex and the right inferior parietal lobule/angular (Fig. 1C; Table 2). Compared to HC group, DC in the right lingual gyrus/calcarine fissure and surrounding cortex (SZ group: 1.21 ± 0.28; OCD group: 1.28 ± 0.28; HC group: 1.51 ± 0.22, Fig. 2C) were significantly lower in both SZ and OCD group and significantly higher in the right inferior parietal lobule/angular gyrus (SZ group: 1.03 ± 0.21; OCD group: 0.83 ± 0.21; HC group: 0.76 ± 0.18, Fig. 2C) in SZ group. Compared to OCD group, DC of SZ group was significantly higher in the right inferior parietal lobule/angular gyrus (P < 0.05, Bonferroni correction).

Correlations with clinical scores and illness duration

In SZ group, ALFF in the left postcentral gyrus was positively correlated with PANSS positive subscale score (r = 0.588, P = 0.013, Fig. 3A) and PANSS general psychopathological subscale score (r = 0.488, P = 0.047, Fig. 3A), respectively. ALFF in the left superior temporal gyrus/insula/rolandic operculum of participants with OCD was positively correlated with compulsion subscale score (r = 0.463, P = 0.030, Fig. 3B). No significant correlation was found between ReHo and DC and the severity of clinical symptoms, respectively.

In SZ group, the longer the illness duration, the lower the ALFF of the left superior temporal gyrus/insula/rolandic operculum (Rho = 0.-492, P = 0.020, Fig. 4A). The longer the illness duration in OCD group, the higher the ALFF of the right supramarginal gyrus/inferior parietal lobule (Rho = 0.392, P = 0.043, Fig. 4B) and the left postcentral gyrus (Rho = 0.385, P = 0.048, Fig. 4C), and the lower the DC of the right inferior parietal lobule/angular gyrus (Rho = − 0.518, P = 0.006, Fig. 4D).

Discussion

In this study, we investigated similar and different spontaneous brain activities between participants with SZ and OCD. We found that both the SZ and OCD groups presented with decreased ALFF in the right supramarginal gyrus/inferior parietal lobule, and decreased DC in the right lingual gyrus/calcarine fissure and surrounding cortex. There were differences in spontaneous brain activity between SZ and OCD in the frontal, temporal, parietal, occipital, and insula regions. The exploratory correlation analyses showed that ALFF and DC of certain brain regions were associated with the severity of clinical symptoms and duration of illness in participants with SZ and OCD.

Altered spontaneous brain activities in SZ and OCD respectively

This study showed that OCD group had abnormal ALFF in the frontal lobe, angular gyrus, insula, temporal lobe, and rolandic operculum, where SZ group had no significantly abnormal ALFF, compared to HC group. Consistent with the results of this study, previous studies have shown that participants with OCD have abnormalities in brain regions not only within the classic cortico-striato-thalamo-cortical (CSTC) circuit but also outside the CSTC circuit [14]. Our findings provide broader evidence that brain regions outside the CSTC circuits are involved in the pathophysiology of OCD. However, the results of this study did not demonstrate the damage of spontaneous brain activities in the common CSTC circuits such as the orbitofrontal lobe, thalamus, and anterior cingulate gyrus in participants with OCD, which may be ascribed to the difference in sample size and research method.

In the right inferior parietal gyrus/angular gyrus, DC was significantly increased in SZ group, which, however, was not significantly altered in OCD group. The inferior parietal lobule, including the supramarginal gyrus and angular gyrus, is a major network hub of the human brain and plays an important role in a wide range of behaviors and functions from bottom-up perception to social cognition. Previous reviews suggested that the impairments of the inferior parietal lobule in participants with SZ mainly affect their body image, sensory integration, self-concept, and executive function [37].

Differences in spontaneous brain activities between SZ and OCD

Compared with HC group, ReHo of the right angular gyrus/middle occipital gyrus was significantly higher in OCD group and lower in SZ group. These present findings are compatible with the previous functional alterations results. Recent researches have suggested that the angular gyrus is responsible for complex mental phenomena and processes, such as understanding visual and audio inputs [38], interpreting languages [39], retrieving memories [40], and maintaining consciousness [41]. Moreover, the angular gyrus has been demonstrated to be one of the overlapping regions between the DMN and social brain networks [42]. Niu et al. [43]. demonstrated that higher ReHo was found in the left angular gyrus in participants with OCD. Nierenberg et al. [44]. found that volume of the left angular gyrus in participants with new-onset SZ was smaller than that in healthy subjects and proposed that the angular gyrus may be the neuroanatomical substrate of the expression of SZ. The occipital cortex is considered to play an important role in early visual processing, such as visual hallucinations and object-recognition defects [45]. Fan et al. found that participants with OCD had higher ALFF in the right middle occipital gyrus [46]. Moreover, the occipital cortex was also demonstrated to play an important role in OCD by several previous studies [46, 47]. Yu et al. [48]. reported lower ReHo in the occipital lobe in participants with SZ. Therefore, the distinct patterns of ReHo in the right middle occipital gyrus may indicate differently impaired visual processing in the two different diseases, which is compatible with the previous findings on the eye movement characteristics of participants with SZ and OCD [49]. Wang et al. [18] used the resting-state functional connectivity (rsFC) method to find that SZ group had increased rsFC between the middle temporal gyrus and the subregions of the DMN, where OCD group exhibited decreased rsFC. The rsFC between the subregions of DMN and executive control network (ECN) increased in both SZ and OCD groups. A previous comparative study using probabilistic tractography found that compared with OCD, SZ exhibited increased connection probability within the right middle occipital gyrus, and between the left middle occipital gyrus and the left middle temporal gyrus [50].

Shared spontaneous brain activity alterations between SZ and OCD

We detected that both the SZ and OCD groups had decreased ALFF in the right supramarginal gyrus/inferior parietal lobule and decreased DC in the right lingual gyrus/calcarine fissure and surrounding cortex. The inferior parietal lobule is a major network hub of the human brain and plays an important role in a wide range of behaviors and functions from bottom-up perception to social cognition [51, 52]. Previous studies have also found abnormalities in the structure or function of the inferior parietal lobule in SZ and OCD, respectively [37, 53]. The lingual gyrus and calcarine fissure and surrounding cortex are located in the occipital lobe and are closely related to visual information processing. The lingual gyrus, an important part of the visual recognition network, plays a role in mediating visual word processing and analyzing the complex features of visual forms and also participates in emotion perception during facial stimulation, especially facial recognition [54]. Several studies have observed structural or functional abnormalities of the lingual gyrus or occipital lobe in both SZ and OCD [55‐59]. The meta-analysis of Gao et al. [56] showed structural abnormalities in the lingual gyrus in drug-free participants with SZ. Moreira et al. [55] observed that participants with OCD displayed reduced functional connectivity within and between visual and sensorimotor networks.

Overall comparison of altered spontaneous brain activities between SZ and OCD

In general, the results of this study showed that SZ and OCD had some similarities in the spontaneous brain activity in the parietal and occipital lobes, but exhibited different spontaneous brain activity patterns in the frontal, temporal, parietal, occipital, and insular lobes. Although there is increasing evidence that SZ and OCD share neurobiological abnormalities [3, 6], some studies have failed to find overlap between them. Previous studies have shown that SZ is a more severe biological disturbance with greater neurological abnormalities than OCD, and SZ and OCD may have different potential neurobiological mechanisms [4, 5, 60]. Our previous studies investigated the association between SZ and OCD from the perspective of the topological organization of the white matter (WM) network and found that SZ exhibits a wide range of abnormal patterns involving the frontal, parietal, occipital, temporal, and subcortical regions [5].

It is noteworthy that this study showed that there were more significant ALFF alterations in OCD and more significant DC alterations in SZ. For a single voxel, its neural activity intensity was characterized with ALFF, and its importance in complex brain networks is revealed with DC. Therefore, these dissimilarities between SZ and OCD suggested that the two disorders may have distinct patterns of spontaneous brain activity impairments and that SZ implicates more abnormalities in functional connections among brain regions.

Correlations among ALFF, symptoms severity and course of disease in SZ and OCD respectively

Our findings showed that ALFF in the left postcentral gyrus was positively associated with the severity of clinical symptoms expressed by positive subscale score and general psychopathological subscale score respectively on the PANSS in participants with SZ, and ALFF in the left superior temporal gyrus, insula, and rolandic operculum was positively associated with the severity of clinical symptoms presented by compulsion subscale score and total score on the Y-BOCS in participants with OCD. These present findings are compatible with the previous studies. Qiu et al. [61] reported that abnormal gray matter density was shown in the left postcentral gyrus, where the abnormal gray matter density was correlated with RSS, a specific eye movement index of schizophrenia, which is associated with the integration of several perceptual/cognitive processes, including selective and sustained attention, and working memory [62‐64], reflecting the clinical hallucination severity of schizophrenia [65]. As for OCD, the superior temporal gyrus was documented to be specifically associated with social anhedonia in OCD [66]. Moreover, greater recruitment of the left superior temporal gyrus was found in pediatric participants with OCD than HC during combined symptom provocation, which suggested the involvement of the temporal poles in pediatric OCD during symptom provocation [67].

To increase the credibility of the initial results, the correlation method was employed to correlate measures of brain activity with symptom severity and illness duration, respectively, in our present study. Results from these correlation analyses demonstrated the longer the illness duration in SZ, the lower the ALFF of the left superior temporal gyrus/insula/rolandic operculum. A previous study reported a progressive gray matter volume reduction in the left posterior superior temporal gyrus in participants with first-episode SZ [68]. Keshavan et al. [69] also found that the duration of pretreatment illness was negatively correlated with the volume of the left superior temporal gyrus, and this correlation was only limited to men. In addition, we observed the longer the illness duration in OCD group, the higher the ALFF of the right supramarginal gyrus/inferior parietal lobule and the left postcentral gyrus, and the lower the DC of the right inferior parietal lobule/angular gyrus. These results suggested that the duration of illness in SZ and OCD may influence spontaneous brain activity in some brain regions.

Limitations

The present study has some potential limitations. First, the sample size used for imaging analyses in this study is relatively small, which may limit the value of the research. For example, due to the limitation of the sample size, difficulties in subdividing participants with SZ and OCD respectively into different groups based on symptoms or subtypes increased, resulting in a lack of full consideration of the heterogeneity of the sample. As for sample size estimation, we could not calculate the power of the study in a scientific way. Second, the disadvantage is that the three groups of subjects in our present study are unevenly matched in terms of the number of participants, age, and education level. Third, the HC group in this study lacked PANSS, Y-BOCS, HARS, and 24-HDRS for comparison. In addition, it was reported that different standardized procedures may affect the re-test reliability of ALFF, ReHo and DC [70]. Future studies should recruit untreated people with first-episode SZ and OCD, as well as add a schizo-obsessive [2] group to further investigate the characteristics of brain imaging changes in SZ and OCD.

Conclusions

In summary, our data demonstrated that SZ and OCD show some similarities in spontaneous brain activity in parietal and occipital lobes, but exhibit different patterns of spontaneous brain activity in frontal, temporal, parietal, occipital, and insula lobes, which might reveal that SZ and OCD have different underlying neurobiological mechanisms. In OCD, there are more significant spontaneous brain activity alterations in local brain regions in the resting state, while in SZ, there are more significant functional connections alterations between individual brain regions and other brain regions. Moreover, the exploratory correlation analyses showed that both ALFF and DC in certain brain regions were correlated with the severity of clinical symptoms or illness duration in participants with SZ and OCD respectively.

Acknowledgments

The authors would like to thank all subjects for contributing their time and efforts to this study.

Declarations

This study was approved by the Medical Ethics Committee of Wuxi Mental Health Center, Nanjing Medical University, China. All participants provided written informed consent.

Not applicable.

Competing interests

The authors declare that they have no conflicts of interest in this work.

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Meier SM, Petersen L, Pedersen MG, Arendt MC, Nielsen PR, Mattheisen M, et al. Obsessive-compulsive disorder as a risk factor for schizophrenia: a nationwide study. JAMA psychiatry. 2014;71(11):1215–21. https://doi.org/10.1001/jamapsychiatry.2014.1011.CrossRefPubMed

Poyurovsky M, Zohar J, Glick I, Koran LM, Weizman R, Tandon R, et al. Obsessive-compulsive symptoms in schizophrenia: implications for future psychiatric classifications. Compr Psychiatry. 2012;53(5):480–3. https://doi.org/10.1016/j.comppsych.2011.08.009.CrossRefPubMed

Patel Y, Parker N, Shin J, Howard D, French L, Thomopoulos SI, et al. Virtual histology of cortical thickness and shared neurobiology in 6 psychiatric disorders. JAMA psychiatry. 2021;78(1):47–63. https://doi.org/10.1001/jamapsychiatry.2020.2694.CrossRefPubMed

Ha TH, Yoon U, Lee KJ, Shin YW, Lee JM, Kim IY, et al. Fractal dimension of cerebral cortical surface in schizophrenia and obsessive-compulsive disorder. Neurosci Lett. 2005;384(1–2):172–6. https://doi.org/10.1016/j.neulet.2005.04.078.CrossRefPubMed

Qin J, Sui J, Ni H, Wang S, Zhang F, Zhou Z, et al. The shared and distinct white matter networks between drug-naive patients with obsessive-compulsive disorder and schizophrenia. Front Neurosci. 2019;13:96. https://doi.org/10.3389/fnins.2019.00096.CrossRefPubMedPubMedCentral

Hawco C, Voineskos AN, Radhu N, Rotenberg D, Ameis S, Backhouse FA, et al. Age and gender interactions in white matter of schizophrenia and obsessive compulsive disorder compared to non-psychiatric controls: commonalities across disorders. Brain imaging and behavior. 2017;11(6):1836–48. https://doi.org/10.1007/s11682-016-9657-8.CrossRefPubMed

Lee MH, Smyser CD, Shimony JS. Resting-state fMRI: a review of methods and clinical applications. AJNR Am J Neuroradiol. 2013;34(10):1866–72. https://doi.org/10.3174/ajnr.A3263.CrossRefPubMedPubMedCentral

Rosazza C, Minati L. Resting-state brain networks: literature review and clinical applications. Neurological sciences : official journal of the Italian Neurological Society and of the Italian Society of Clinical Neurophysiology. 2011;32(5):773–85. https://doi.org/10.1007/s10072-011-0636-y.CrossRef

Liu C, Xue Z, Palaniyappan L, Zhou L, Liu H, Qi C, et al. Abnormally increased and incoherent resting-state activity is shared between patients with schizophrenia and their unaffected siblings. Schizophr Res. 2016;171(1–3):158–65. https://doi.org/10.1016/j.schres.2016.01.022.CrossRefPubMed

10.

Zhang Z, Bo Q, Li F, Zhao L, Wang Y, Liu R, et al. Increased ALFF and functional connectivity of the right striatum in bipolar disorder patients. Prog Neuro-Psychopharmacol Biol Psychiatry. 2020;110140:110140. https://doi.org/10.1016/j.pnpbp.2020.110140.CrossRef

11.

Zang Y, Jiang T, Lu Y, He Y, Tian L. Regional homogeneity approach to fMRI data analysis. NeuroImage. 2004;22(1):394–400. https://doi.org/10.1016/j.neuroimage.2003.12.030.CrossRefPubMed

12.

Ji L, Meda SA, Tamminga CA, Clementz BA, Keshavan MS, Sweeney JA, et al. Characterizing functional regional homogeneity (ReHo) as a B-SNIP psychosis biomarker using traditional and machine learning approaches. Schizophr Res. 2020;215:430–8. https://doi.org/10.1016/j.schres.2019.07.015.CrossRefPubMed

13.

Zuo XN, Ehmke R, Mennes M, Imperati D, Castellanos FX, Sporns O, Milham MP. Network centrality in the human functional connectome. Cerebral cortex (New York, NY : 1991). 2012; 22(8):1862–1875.

14.

Tian L, Meng C, Jiang Y, Tang Q, Wang S, Xie X, et al. Abnormal functional connectivity of brain network hubs associated with symptom severity in treatment-naive patients with obsessive-compulsive disorder: a resting-state functional MRI study. Prog Neuro-Psychopharmacol Biol Psychiatry. 2016;66:104–11. https://doi.org/10.1016/j.pnpbp.2015.12.003.CrossRef

15.

Gross-Isseroff R, Hermesh H, Zohar J, Weizman A. Neuroimaging communality between schizophrenia and obsessive compulsive disorder: a putative basis for schizo-obsessive disorder? The world journal of biological psychiatry : the official journal of the World Federation of Societies of Biological Psychiatry. 2003;4(3):129–34. https://doi.org/10.1080/15622970310029907.CrossRef

16.

Goodkind M, Eickhoff SB, Oathes DJ, Jiang Y, Chang A, Jones-Hagata LB, et al. Identification of a common neurobiological substrate for mental illness. JAMA psychiatry. 2015;72(4):305–15. https://doi.org/10.1001/jamapsychiatry.2014.2206.CrossRefPubMedPubMedCentral

17.

Kim MS. Hyon T, Kwon, Soo JJCOiP. Neurological abnormalities in schizophrenia and obsessive-compulsive disorder. Current Opinion in Psychiatry. 2004;17(3):215–20. https://doi.org/10.1097/00001504-200405000-00011.CrossRef

18.

Fan J, Gan J, Liu W, Zhong M, Liao H, Zhang H, et al. Resting-state default mode network related functional connectivity is associated with sustained attention deficits in schizophrenia and obsessive-compulsive disorder. Front Behav Neurosci. 2018;12:319. https://doi.org/10.3389/fnbeh.2018.00319.CrossRefPubMedPubMedCentral

19.

Wang YM, Zou LQ, Xie WL, Yang ZY, Zhu XZ, Cheung EFC, et al. Altered functional connectivity of the default mode network in patients with Schizo-obsessive comorbidity: a comparison between schizophrenia and obsessive-compulsive disorder. Schizophr Bull. 2019;45(1):199–210. https://doi.org/10.1093/schbul/sbx194.CrossRefPubMed

20.

Lu XB, Zhang Y, Yang DY, Yang YZ, Wu FC, Ning YP, et al. Analysis of first-episode and chronic schizophrenia using multi-modal magnetic resonance imaging. Eur Rev Med Pharmacol Sci. 2018;22(19):6422–35. https://doi.org/10.26355/eurrev_201810_16055.CrossRefPubMed

21.

Xia J, Fan J, Liu W, Du H, Zhu J, Yi J, et al. Functional connectivity within the salience network differentiates autogenous- from reactive-type obsessive-compulsive disorder. Prog Neuro-Psychopharmacol Biol Psychiatry. 2020;98:109813. https://doi.org/10.1016/j.pnpbp.2019.109813.CrossRef

22.

Association) AP: Diagnostic and Statistical Manual of Mental Disorders (DSM-IV-TR): Diagnostic and Statistical Manual of Mental Disorders (DSM-IV-TR); 2000.

23.

Kay SR, Fiszbein A, Opler LA. The positive and negative syndrome scale (PANSS) for schizophrenia. Schizophr Bull. 1987;13(2):261–76. https://doi.org/10.1093/schbul/13.2.261.CrossRefPubMed

24.

Goodman WK, Price LH, Rasmussen SA, Mazure C, Fleischmann RL, Hill CL, et al. The Yale-Brown obsessive compulsive scale. I. Development, use, and reliability. Arch Gen Psychiatry. 1989;46(11):1006–11. https://doi.org/10.1001/archpsyc.1989.01810110048007.CrossRefPubMed

25.

Hamilton M. The assessment of anxiety states by rating. The British journal of medical psychology. 1959;32(1):50–5. https://doi.org/10.1111/j.2044-8341.1959.tb00467.x.CrossRefPubMed

26.

Hamilton M. Development of a rating scale for primary depressive illness. The British journal of social and clinical psychology. 1967;6(4):278–96. https://doi.org/10.1111/j.2044-8260.1967.tb00530.x.CrossRefPubMed

27.

Oldfield RC. The assessment and analysis of handedness: the Edinburgh inventory. Neuropsychologia. 1971;9(1):97–113. https://doi.org/10.1016/0028-3932(71)90067-4.CrossRefPubMed

28.

Yan CG, Wang XD, Zuo XN, Zang YF. DPABI: Data Processing & Analysis for (resting-state) brain imaging. Neuroinformatics. 2016;14(3):339–51. https://doi.org/10.1007/s12021-016-9299-4.CrossRefPubMed

29.

Power JD, Barnes KA, Snyder AZ, Schlaggar BL, Petersen SE. Spurious but systematic correlations in functional connectivity MRI networks arise from subject motion. NeuroImage. 2012;59(3):2142–54. https://doi.org/10.1016/j.neuroimage.2011.10.018.CrossRefPubMed

30.

Yan CG, Cheung B, Kelly C, Colcombe S, Craddock RC, Di Martino A, et al. A comprehensive assessment of regional variation in the impact of head micromovements on functional connectomics. NeuroImage. 2013;76:183–201. https://doi.org/10.1016/j.neuroimage.2013.03.004.CrossRefPubMed

31.

Friston KJ, Williams S, Howard R, Frackowiak RS, Turner R. Movement-related effects in fMRI time-series. Magn Reson Med. 1996;35(3):346–55. https://doi.org/10.1002/mrm.1910350312.CrossRefPubMed

32.

Chao-Gan Y, Yu-Feng Z. DPARSF: a MATLAB toolbox for "pipeline" data analysis of resting-state fMRI. Front Syst Neurosci. 2010;4:13.PubMedPubMedCentral

33.

Lv Y, Li L, Song Y, Han Y, Zhou C, Zhou D, et al. The local brain abnormalities in patients with transient ischemic attack: a resting-state fMRI study. Front Neurosci. 2019;13:24. https://doi.org/10.3389/fnins.2019.00024.CrossRefPubMedPubMedCentral

34.

Zhao N, Yuan LX, Jia XZ, Zhou XF, Deng XP, He HJ, et al. Intra- and inter-scanner reliability of voxel-wise whole-brain analytic metrics for resting state fMRI. Frontiers in neuroinformatics. 2018;12:54. https://doi.org/10.3389/fninf.2018.00054.CrossRefPubMedPubMedCentral

35.

Beucke JC, Sepulcre J, Talukdar T, Linnman C, Zschenderlein K, Endrass T, et al. Abnormally high degree connectivity of the orbitofrontal cortex in obsessive-compulsive disorder. JAMA psychiatry. 2013;70(6):619–29. https://doi.org/10.1001/jamapsychiatry.2013.173.CrossRefPubMed

36.

Deng W, Zhang B, Zou W, Zhang X, Cheng X, Guan L, et al. Abnormal degree centrality associated with cognitive dysfunctions in early bipolar disorder. Frontiers in psychiatry. 2019;10:140. https://doi.org/10.3389/fpsyt.2019.00140.CrossRefPubMedPubMedCentral

37.

Torrey EF. Schizophrenia and the inferior parietal lobule. Schizophr Res. 2007;97(1–3):215–25. https://doi.org/10.1016/j.schres.2007.08.023.CrossRefPubMed

38.

Pratt H, Bleich N, Mittelman N. Spatio-temporal distribution of brain activity associated with audio-visually congruent and incongruent speech and the McGurk effect. Brain and behavior. 2015;5(11):e00407. https://doi.org/10.1002/brb3.407.CrossRefPubMedPubMedCentral

39.

Luthra S, Correia JM, Kleinschmidt DF, Mesite L, Myers EB. Lexical information guides retuning of neural patterns in perceptual learning for speech. J Cogn Neurosci. 2020;32(10):2001–12. https://doi.org/10.1162/jocn_a_01612.CrossRefPubMedPubMedCentral

40.

Branzi FM, Pobric G, Jung J, Lambon Ralph MA. The left angular Gyrus is causally involved in context-dependent integration and associative encoding during narrative Reading. J Cogn Neurosci. 2021:1–14.

41.

Scheinin A, Kantonen O, Alkire M, Långsjö J, Kallionpää RE, Kaisti K, et al. Foundations of human consciousness: imaging the twilight zone. J Neurosci. 2021;41(8):1769–78. https://doi.org/10.1523/JNEUROSCI.0775-20.2020.CrossRefPubMedPubMedCentral

42.

Schilbach L, Eickhoff SB, Rotarska-Jagiela A, Fink GR, Vogeley K. Minds at rest? Social cognition as the default mode of cognizing and its putative relationship to the "default system" of the brain. Conscious Cogn. 2008;17(2):457–67. https://doi.org/10.1016/j.concog.2008.03.013.CrossRefPubMed

43.

Niu Q, Yang L, Song X, Chu C, Liu H, Zhang L, et al. Abnormal resting-state brain activities in patients with first-episode obsessive-compulsive disorder. Neuropsychiatr Dis Treat. 2017;13:507–13. https://doi.org/10.2147/NDT.S117510.CrossRefPubMedPubMedCentral

44.

Nierenberg J, Salisbury DF, Levitt JJ, David EA, McCarley RW, Shenton ME. Reduced left angular gyrus volume in first-episode schizophrenia. Am J Psychiatry. 2005;162(8):1539–41. https://doi.org/10.1176/appi.ajp.162.8.1539.CrossRefPubMedPubMedCentral

45.

Onitsuka T, McCarley RW, Kuroki N, Dickey CC, Kubicki M, Demeo SS, et al. Occipital lobe gray matter volume in male patients with chronic schizophrenia: a quantitative MRI study. Schizophr Res. 2007;92(1–3):197–206. https://doi.org/10.1016/j.schres.2007.01.027.CrossRefPubMedPubMedCentral

46.

Fan J, Zhong M, Gan J, Liu W, Niu C, Liao H, et al. Spontaneous neural activity in the right superior temporal gyrus and left middle temporal gyrus is associated with insight level in obsessive-compulsive disorder. J Affect Disord. 2017;207:203–11. https://doi.org/10.1016/j.jad.2016.08.027.CrossRefPubMed

47.

Piras F, Piras F, Chiapponi C, Girardi P, Caltagirone C, Spalletta G. Widespread structural brain changes in OCD: a systematic review of voxel-based morphometry studies. Cortex; a journal devoted to the study of the nervous system and behavior. 2015;62:89–108. https://doi.org/10.1016/j.cortex.2013.01.016.CrossRefPubMed

48.

Yu R, Hsieh MH, Wang HL, Liu CM, Liu CC, Hwang TJ, et al. Frequency dependent alterations in regional homogeneity of baseline brain activity in schizophrenia. PLoS One. 2013;8(3):e57516. https://doi.org/10.1371/journal.pone.0057516.CrossRefPubMedPubMedCentral

49.

Farber RH, Clementz BA, Swerdlow NR. Characteristics of open- and closed-loop smooth pursuit responses among obsessive-compulsive disorder, schizophrenia, and nonpsychiatric individuals. Psychophysiology. 1997;34(2):157–62. https://doi.org/10.1111/j.1469-8986.1997.tb02126.x.CrossRefPubMed

50.

Wang YM, Yang ZY, Cai XL, Zhou HY, Zhang RT, Yang HX, et al. Identifying Schizo-obsessive comorbidity by tract-based spatial statistics and probabilistic Tractography. Schizophr Bull. 2020;46(2):442–53. https://doi.org/10.1093/schbul/sbz073.CrossRefPubMed

51.

Stern ER, Fitzgerald KD, Welsh RC, Abelson JL, Taylor SF. Resting-state functional connectivity between fronto-parietal and default mode networks in obsessive-compulsive disorder. PLoS One. 2012;7(5):e36356. https://doi.org/10.1371/journal.pone.0036356.CrossRefPubMedPubMedCentral

52.

Zhang T, Wang J, Yang Y, Wu Q, Li B, Chen L, et al. Abnormal small-world architecture of top-down control networks in obsessive-compulsive disorder. Journal of psychiatry & neuroscience : JPN. 2011;36(1):23–31. https://doi.org/10.1503/jpn.100006.CrossRef

53.

Boedhoe PSW, Schmaal L, Abe Y, Alonso P, Ameis SH, Anticevic A, et al. Cortical abnormalities associated with pediatric and adult obsessive-compulsive disorder: findings from the ENIGMA obsessive-compulsive disorder working group. Am J Psychiatry. 2018;175(5):453–62. https://doi.org/10.1176/appi.ajp.2017.17050485.CrossRefPubMed

54.

Szeszko PR, Ardekani BA, Ashtari M, Malhotra AK, Robinson DG, Bilder RM, et al. White matter abnormalities in obsessive-compulsive disorder: a diffusion tensor imaging study. Arch Gen Psychiatry. 2005;62(7):782–90. https://doi.org/10.1001/archpsyc.62.7.782.CrossRefPubMed

55.

Moreira PS, Marques P, Magalhães R, Esteves M, Sousa N, Soares JM, et al. The resting-brain of obsessive-compulsive disorder. Psychiatry research Neuroimaging. 2019;290:38–41. https://doi.org/10.1016/j.pscychresns.2019.06.008.CrossRefPubMed

56.

Gao X, Zhang W, Yao L, Xiao Y, Liu L, Liu J, et al. Association between structural and functional brain alterations in drug-free patients with schizophrenia: a multimodal meta-analysis. Journal of psychiatry & neuroscience : JPN. 2018;43(2):131–42. https://doi.org/10.1503/jpn.160219.CrossRef

57.

Moreira PS, Marques P, Soriano-Mas C, Magalhães R, Sousa N, Soares JM, et al. The neural correlates of obsessive-compulsive disorder: a multimodal perspective. Transl Psychiatry. 2017;7(8):e1224. https://doi.org/10.1038/tp.2017.189.CrossRefPubMedPubMedCentral

58.

Gonçalves OF, Marques TR, Lori NF, Sampaio A, Branco MC. Obsessive-compulsive disorder as a visual processing impairment. Med Hypotheses. 2010;74(1):107–9. https://doi.org/10.1016/j.mehy.2009.07.048.CrossRefPubMed

59.

Revheim N, Butler PD, Schechter I, Jalbrzikowski M, Silipo G, Javitt DC. Reading impairment and visual processing deficits in schizophrenia. Schizophr Res. 2006;87(1–3):238–45. https://doi.org/10.1016/j.schres.2006.06.022.CrossRefPubMedPubMedCentral

60.

Riffkin J, Yücel M, Maruff P, Wood SJ, Soulsby B, Olver J, et al. A manual and automated MRI study of anterior cingulate and orbito-frontal cortices, and caudate nucleus in obsessive-compulsive disorder: comparison with healthy controls and patients with schizophrenia. Psychiatry Res. 2005;138(2):99–113. https://doi.org/10.1016/j.pscychresns.2004.11.007.CrossRefPubMed

61.

Qiu L, Tian L, Pan C, Zhu R, Liu Q, Yan J, et al. Neuroanatomical circuitry associated with exploratory eye movement in schizophrenia: a voxel-based morphometric study. PLoS One. 2011;6(10):e25805. https://doi.org/10.1371/journal.pone.0025805.CrossRefPubMedPubMedCentral

62.

Matsue Y, Okuma T, Saito H, Aneha S, Ueno T, Chiba H, et al. Saccadic eye movements in tracking, fixation, and rest in schizophrenic and normal subjects. Biol Psychiatry. 1986;21(4):382–9. https://doi.org/10.1016/0006-3223(86)90166-6.CrossRefPubMed

63.

Matsushima E, Kojima T, Ohta K, Obayashi S, Nakajima K, Kakuma T, et al. Exploratory eye movement dysfunctions in patients with schizophrenia: possibility as a discriminator for schizophrenia. J Psychiatr Res. 1998;32(5):289–95. https://doi.org/10.1016/S0022-3956(98)00019-3.CrossRefPubMed

64.

Suzuki M, Takahashi S, Matsushima E, Tsunoda M, Kurachi M, Okada T, et al. Exploratory eye movement dysfunction as a discriminator for schizophrenia : a large sample study using a newly developed digital computerized system. Eur Arch Psychiatry Clin Neurosci. 2009;259(3):186–94. https://doi.org/10.1007/s00406-008-0850-7.CrossRefPubMed

65.

Qiu L, Yan H, Zhu R, Yan J, Yuan H, Han Y, et al. Correlations between exploratory eye movement, hallucination, and cortical gray matter volume in people with schizophrenia. BMC psychiatry. 2018;18(1):226. https://doi.org/10.1186/s12888-018-1806-8.CrossRefPubMedPubMedCentral

66.

Xia J, Fan J, Du H, Liu W, Li S, Zhu J, et al. Abnormal spontaneous neural activity in the medial prefrontal cortex and right superior temporal gyrus correlates with anhedonia severity in obsessive-compulsive disorder. J Affect Disord. 2019;259:47–55. https://doi.org/10.1016/j.jad.2019.08.019.CrossRefPubMed

67.

Jaspers-Fayer F, Lin SY, Chan E, Ellwyn R, Lim R, Best J, et al. Neural correlates of symptom provocation in pediatric obsessive-compulsive disorder. NeuroImage Clinical. 2019;24:102034. https://doi.org/10.1016/j.nicl.2019.102034.CrossRefPubMedPubMedCentral

68.

Kasai K, Shenton ME, Salisbury DF, Hirayasu Y, Lee CU, Ciszewski AA, et al. Progressive decrease of left superior temporal gyrus gray matter volume in patients with first-episode schizophrenia. Am J Psychiatry. 2003;160(1):156–64. https://doi.org/10.1176/appi.ajp.160.1.156.CrossRefPubMedPubMedCentral

69.

Keshavan MS, Haas GL, Kahn CE, Aguilar E, Dick EL, Schooler NR, et al. Superior temporal gyrus and the course of early schizophrenia: progressive, static, or reversible? J Psychiatr Res. 1998;32(3–4):161–7. https://doi.org/10.1016/S0022-3956(97)00038-1.CrossRefPubMed

70.

Yan CG, Craddock RC, Zuo XN, Zang YF, Milham MP. Standardizing the intrinsic brain: towards robust measurement of inter-individual variation in 1000 functional connectomes. NeuroImage. 2013;80:246–62. https://doi.org/10.1016/j.neuroimage.2013.04.081.CrossRefPubMed

Titel: Comparison of resting-state spontaneous brain activity between treatment-naive schizophrenia and obsessive-compulsive disorder
verfasst von: Xiao-Man Yu
Lin-Lin Qiu
Hai-Xia Huang
Xiang Zuo
Zhen-He Zhou
Shuai Wang
Hai-Sheng Liu
Lin Tian
Publikationsdatum: 01.12.2021
Verlag: BioMed Central
Erschienen in: BMC Psychiatry / Ausgabe 1/2021
Elektronische ISSN: 1471-244X
DOI: https://doi.org/10.1186/s12888-021-03554-y

Neu im Fachgebiet Psychiatrie

Demenzkranke durch Antipsychotika vielfach gefährdet

23.04.2024 Demenz Nachrichten

Wenn Demenzkranke aufgrund von Symptomen wie Agitation oder Aggressivität mit Antipsychotika behandelt werden, sind damit offenbar noch mehr Risiken verbunden als bislang angenommen.

Weniger postpartale Depressionen nach Esketamin-Einmalgabe

23.04.2024 Depression antepartal oder postpartal Nachrichten

Bislang gibt es kein Medikament zur Prävention von Wochenbettdepressionen. Das Injektionsanästhetikum Esketamin könnte womöglich diese Lücke füllen.

„Psychotherapie ist auch bei sehr alten Menschen hochwirksam!“

22.04.2024 DGIM 2024 Kongressbericht

Die Kombination aus Medikamenten und Psychotherapie gilt als effektivster Ansatz bei Depressionen. Das ist bei betagten Menschen nicht anders, trotz Besonderheiten.

Auf diese Krankheiten bei Geflüchteten sollten Sie vorbereitet sein

22.04.2024 DGIM 2024 Nachrichten

Um Menschen nach der Flucht aus einem Krisengebiet bestmöglich medizinisch betreuen zu können, ist es gut zu wissen, welche Erkrankungen im jeweiligen Herkunftsland häufig sind. Dabei hilft eine Internetseite der CDC (Centers for Disease Control and Prevention).

Springer Medizin

Abstract

Background

Methods

Results

Conclusion

Publisher’s Note

Background

Methods

Participants

Data acquisition

Data preprocessing

ALFF analyses

ReHo and DC analyses

Statistical analyses

Results

Demographics and clinical characteristics

ALFF differences among the three groups

ReHo differences among the three groups

DC differences among the three groups

Correlations with clinical scores and illness duration

Discussion

Altered spontaneous brain activities in SZ and OCD respectively

Differences in spontaneous brain activities between SZ and OCD

Shared spontaneous brain activity alterations between SZ and OCD

Overall comparison of altered spontaneous brain activities between SZ and OCD

Correlations among ALFF, symptoms severity and course of disease in SZ and OCD respectively

Limitations

Conclusions

Acknowledgments

Declarations

Ethics approval and consent to participate

Consent for publication

Competing interests

Publisher’s Note

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Auf diese Krankheiten bei Geflüchteten sollten Sie vorbereitet sein