metricas
covid
Buscar en
International Journal of Clinical and Health Psychology
Toda la web
Inicio International Journal of Clinical and Health Psychology Hypoconnectivity networks in schizophrenia patients: A voxel-wise meta-analysis ...
Información de la revista
Vol. 23. Núm. 4.
(octubre - diciembre 2023)
Compartir
Compartir
Descargar PDF
Más opciones de artículo
Visitas
1188
Vol. 23. Núm. 4.
(octubre - diciembre 2023)
Acceso a texto completo
Hypoconnectivity networks in schizophrenia patients: A voxel-wise meta-analysis of Rs-fMRI
Visitas
1188
Silvia Ruiz-Torrasd, Esteve Gudayol-Ferrée, Oscar Fernández-Vazqueza, Cristina Cañete-Masséa,b, Maribel Peró-Cebolleroa,b,c, Joan Guàrdia-Olmosa,b,c,
Autor para correspondencia
jguardia@ub.edu

Corresponding author.
a Facultat de Psicologia, Secció de Psicologia Quantitativa, Universitat de Barcelona, Spain
b UB Institute of Complex Systems, Universitat de Barcelona, Spain
c Institute of Neuroscience, Universitat de Barcelona, Spain
d Clínica Psicològica de la Universitat de Barcelona, Fundació Josep Finestres, Universitat de Barcelona, Spain
e Universidad Michoacana San Nicolás de Hidalgo, Mexico
Este artículo ha recibido
Información del artículo
Resumen
Texto completo
Bibliografía
Descargar PDF
Estadísticas
Figuras (6)
Mostrar másMostrar menos
Tablas (7)
Table 1. Descriptive statistics of the observed distributions of mediator quantitative variables.
Table 2. Major differences in activation between patients with schizophrenia and healthy controls in the present meta-analysis.
Table 3. Jackknife sensitivity analysis.
Table 4. Publication bias assessment.
Table 5. Heterogeneity analysis of the positive and negative peaks.
Table 6. Meta-regression of the possible mediator variables and effect sizes.
Mostrar másMostrar menos
Abstract
Background

In recent years several meta-analyses regarding resting-state functional connectivity in patients with schizophrenia have been published. The authors have used different data analysis techniques: regional homogeneity, seed-based data analysis, independent component analysis, and amplitude of low frequencies. Hence, we aim to perform a meta-analysis to identify connectivity networks with different activation patterns between people diagnosed with schizophrenia and healthy controls using voxel-wise analysis. Method: We collected primary studies exploring whole brain connectivity by functional magnetic resonance imaging at rest in patients with schizophrenia compared with healthy controls. We identified 25 studies included high-quality studies that included 1285 patients with schizophrenia and 1279 healthy controls. Results: The results indicate hypoactivation in the right precentral gyrus and the left superior temporal gyrus of patients with schizophrenia compared with healthy controls. Conclusions: These regions have been linked with some clinical symptoms usually present in Plea with schizophrenia, such as auditory verbal hallucinations, formal thought disorder, and the comprehension and production of gestures.

Keywords:
Schizophrenia
fMRI
Resting state
Functional connectivity
Meta-analysis
Texto completo
Introduction

Schizophrenia is a severe, complex, and heterogeneous mental illness that causes severe disturbances in behaviour and cognition. This disease is caused by genetic and environmental factors and usually emerges in adolescence or early adulthood (Owen et al., 2016). Various structural and functional neuroimaging studies have tried to relate the myriad clinical manifestations of this disease with different structural and functional brain anomalies (Van Den Heuvel & Fornito, 2014). A growing body of literature conceptualises schizophrenia as a disorder with a significant dysfunction of brain connectivity within and between different large-scale brain networks.

These connectivity disturbances, in turn, are closely related to the different symptomatic dimensions of schizophrenia (Dong et al., 2018; Friston & Frith, 1995; Li et al., 2019; Van der Heuvel & Fornito, 2014). For example, the severity of positive symptoms has been associated with anomalies in the connectivity strength between the frontal and temporal regions. In contrast, the severity of negative symptoms has been related to decreases in whole-brain functional connectivity (Skudlarski et al., 2010). Researchers have found alterations in the default mode network (DMN), which is related to self-referential thoughts. Some studies have shown that resting-state functional connectivity (rsFC) decreases in patients with schizophrenia, compared with healthy controls (HC), between the posterior cingulate cortex (PCC) and the medial prefrontal lobes, the lateral prefrontal cortex, the parietal cortex, and the insula (Bluhm et al., 2007; Moran et al., 2013). Nevertheless, other authors suggest a pattern of hyperconnectivity within the DMN in this illness (He et al., 2013; Woodward et al., 2011). In addition, the reduced connectivity within the DMN of patients with schizophrenia in the precuneus may correlate with the severity of the negative symptoms, specifically in the apathy domain (Forlim et al., 2020). Other researchers have shown rsFC anomalies in patients with schizophrenia in the cognitive control network (CCN) both at rest (Horne et al., 2021) and when performing cognitive control tasks (Fornito et al., 2011; Ray et al., 2017). As its name suggests, the alterations in this large-scale brain network are closely related to the neuropsychological deficits frequently found in schizophrenia (Ray et al., 2017). The salience network (also known as the core network [CN]) comprises the ACC, the anterior insula, and the dorsolateral prefrontal cortex (DLPFC) and plays an important role in processing the interoception and salience detection of external stimuli (Palaniyappan & Liddle, 2012). Some studies reported that this brain network presents abnormal rs-FC in schizophrenia (Menon et al., 2022; Palaniyappan & Liddle, 2012). Among other symptoms, its alteration may be related to reality distortions frequently found in these patients (Menon et al., 2022; Palaniyappan & Liddle, 2012).

Dysfunctions of the aforementioned brain connectivity networks are related to various groups of schizophrenia symptoms. However, abnormal functioning of other brain connectivity networks has been linked to somewhat more specific symptoms often found in patients with schizophrenia. Thus, alterations in the self-reference network that includes the ventromedial prefrontal cortex, the orbitofrontal cortex, the ACC, and the gyrus rectus could be the basis for the presence of delusions of reference in this disease (Lariviere et al., 2017). Abnormalities in brain areas of the auditory network that include the bilateral middle and superior temporal cortex, Heschl's gyrus, and the temporal pole have been related to the presence of auditory hallucinations (Woodruff et al., 1997). The language network comprises structures mainly in the left hemisphere, such as the inferior frontal gyrus, the frontal operculum, the superior temporal gyrus, the middle temporal gyrus, and the inferior parietal lobe (Friederichi & Gierhan, 2013). Structural and functional alterations within this network and in some other brain areas outside the language network have been associated with formal thought disorder in schizophrenia (Cavelti et al., 2018). Furthermore, alterations in the praxis network, including the superior frontal gyrus, the inferior frontal gyrus, the superior temporal gyrus, the parietal cortex, and the precentral gyrus, among other structures, could be related to the gesture alterations often observed in schizophrenia (Walther, Mittal, et al., 2020).

Likewise, researchers have identified alterations in the connectivity between these cerebral networks. For example, Woodward et al. (2012) suggest that patients with schizophrenia present reduced segregation between the DMN and the CCN compared with HC. In the same study, however, the authors found no differences in the connectivity between the CN and other brain networks. In contrast, Huang et al. (2019) showed evidence of a hyperconnectivity of the CN and the prefrontal cortex and cerebellum, as well as a hypoconnectivity between the cortico-striatal-thalamic-cortical subcircuit and the CN in patients with schizophrenia.

It is clear that in schizophrenia, there are alterations within and between several large-scale rsFC brain networks. In addition, these alterations have been related more or less consistently to various symptoms of the illness. However, the anatomical locations of the connectivity effects and their nature (increases or decreases of connectivity) are less clear. In recent years, some meta-analyses have explored resting-state functional magnetic imaging (rs-fMRI) in patients with schizophrenia compared with HC to identify some regular patterns. For example, Xiao et al. (2017) showed evidence that patients with schizophrenia had increased connectivity, estimated with regional homogeneity (ReHo), in the right superior frontal and right superior temporal gyrus, as well as a decreased ReHo connectivity in the right fusiform gyrus, in the left superior temporal gyrus, the left postcentral gyrus, and the right precentral gyrus. In their meta-analysis, Dong et al. (2018) focused on seed-based studies, showing that patients with schizophrenia present hypoconnectivity in the DMN, the affective network (AN), the ventral attentional network (VAN), the thalamic network (TN), and the somatosensory network. They also showed hypoconnectivity between the VAN and the TN, the VAN and the DMN, the VAN and the CCN, the CCN and the TN, and the CCN and the DMN. They only found hyperconnectivity between AN and VAN. Li et al. (2019) focused on independent component analysis (ICA); their findings support hypoconnectivity in specific brain networks in patients with schizophrenia, specifically the auditory network, the core network, the self-referential network, the DMN, and the somatomotor network. Finally, Gong et al. (2020) showed that patients with schizophrenia present a decreased amplitude of low-frequency fluctuation (ALFF) in the bilateral postcentral gyrus, bilateral precuneus, left inferior parietal gyrus, and right occipital lobe. In addition, they found an increased ALFF in the right putamen, right inferior frontal gyrus, left inferior temporal gyrus and right anterior cingulate cortex.

We aim to perform a meta-analysis of functional connectivity in schizophrenia, focusing on the studies that performed whole-brain connectivity analyses in this disorder. The published meta-analyses in this field have not included the impact of the different analysis techniques (ICA, ReHo, ALFF, fractional amplitude of low-frequency fluctuation [fALFF], and other techniques) on the estimated effect sizes. Therefore, we analyse the possible relationships between the estimated effect size and these potential mediator variables. We also study the possible effect of other mediator variables on estimating the effect sizes: sample size; age; sex; and clinical characteristics of the illness, such as the Positive and Negative Syndrome Scale (PANSS) scores and the duration of schizophrenia.

MethodsStudy selectionData sources

Two independent investigators performed a bibliographic search using the following databases: PubMed, Web of Science, PsycInfo, Google Scholar, and Scopus. Some examples of the Boolean algorithm with the keywords used are presented in the Appendix A. The search included all the studies published until 28 February 2021. We conducted this meta-analysis according to the Preferred Reporting Items for Systematic Reviews and Meta-analysis (PRISMA) guidelines. The inclusion criteria were: (a) published in English or Spanish; (b) full-text was available; (c) primary study in the human population; (d) included a patient group diagnosed with schizophrenia following the Diagnostic and Statistical Manual of Mental Disorders (4th ed.; DSM-IV) criteria or the structured clinical interview for DSM-IV (SCID); (e) compared the brain activation between schizophrenia patients and healthy people; (f) used rs-fMRI; and (g) reported Montreal Neurological Institute (MNI) or Talairach coordinates of the whole brain contrast comparing people with schizophrenia to HC. The exclusion criteria were: (a) systematic reviews or meta-analyses; (b) methodological studies; (c) patients with schizophrenia with other psychiatric or neurological disorders; and (d) studies focused on dynamic connectivity or that used graph analysis or any other technique that does not identify coordinates. Fig. 1 shows the PRISMA flow chart. The search yielded a total of 3763 studies (Web of Science [n = 1123], Scopus [n = 823], PubMed [n = 729], PsycInfo [n = 543], and Google Scholar [n = 345]). Subsequently, we removed 2106 duplicate papers through Mendeley and 64 duplicate papers via Rayyan. In addition, we excluded 1234 studies after title/abstract screening because they did not meet the inclusion criteria. Later, 159 articles were sought to read the full text, but 8 studies could not be found. Thus, after the full-text screening, we excluded 73 studies because they did not indicate the peak's activation coordinates, 16 because they did not report on the statistics associated with the coordinates, and 37 because they did not analyse whole brain. Finally, we included 25 studies meeting the inclusion criteria; they aren’t marked in the refernce list. In addition, there was a 100% rate of agreement between the two investigators for the study search and selection.

Fig. 1.

Flow chart of the search conducted.

(0.55MB).
Voxel-wise meta-analysis

We used the seed-based d Mapping (SDM) software (available at http://www.sdmproject.com) to analyse the differences between patients with schizophrenia and HC. The approach details are described by Müller et al. (2018) and Radua and Mataix (2012). First, we chose the reported peak coordinates of all functional differences that were significant at whole brain level in these studies. We ensured that all included studies used the same statistical threshold throughout the whole brain to avoid possible bias towards regions with liberal thresholds. Thus, we considered the minimum threshold to be defined by a p-value of .001 and Student's t reference value (to minimise the probability of type I error) with the degrees of freedom (df) of each study estimated by the conventional expression (n1 + n2 – 2). Second, we recreated peak coordinates for each study with a standard MNI map of the group differences effect size based on their peak t value by means of a non-normalised Gaussian kernel to the voxels near the peak, which assigns higher values to the voxels closer to peaks. Third, we obtained the mean map by voxel-wise calculation of the study maps random-effects mean, weighted by the sample size. Fourth, to balance sensitivity and specificity correctly, we used a p-value of .05 as the main threshold with an additional peak height of z = 1. We performed jackknife sensitivity analysis to test the replicability of the results. Finally, we considered it appropriate to incorporate papers that have used different estimation techniques. Thus, to the usual estimates based on ALFF, fALFF, or ReHo, we included studies that have proposed some more secondary techniques such as long short functional connectivity (LSFCS), voxel-mirrored homotopic connectivity (VMHC), global function connectivity (GFC), and parameter of asymmetry (PAS). The incorporation of different techniques is justified by the following arguments. First, all of them have a common point, which is the use of linear models in their origin, so the parameter estimates, although different, are based on a very similar model. Second, most studies use ALFF, fALFF, or ReHo, so the rest of the techniques are underrepresented and incorporating them would allow for a more realistic average effect size estimate. This underrepresentation prevents their evaluation as categories of a possible moderating variable, because this type of analysis is limited to comparing between ALFF and ReHo, which are the most common. Finally, there are some antecedents that have applied the same criteria as in this meta-analysis but in other populations of interest. For all these reasons, we considered it would be interesting to incorporate the various estimation techniques (Zhang et al., 2020) also in accordance with what was Kühn and Gallinat (2013) proposed with fALFF and ICA.

After calculating Cohen's d and the confidence intervals (CI) for the papers, we conducted a descriptive analysis of each paper to clarify the results. To control false positives, we used the correction described by Albajes-Eizagirre et al. (2021), which is based on the permutation of images to minimise the error.

Quality assessment

We assessed the quality of the included studies by using a checklist consisting of 11 items that focused on the clinical characteristics of the participants, the neuroimaging and data analysis methodology, the results, and the conclusions of the studies. The Quality Assessment scale is shown in Appendix B. This checklist is based on previous meta-analyses and has been described elsewhere (Chen et al., 2015; Shepherd et al., 2012). One author reviewed the included studies and determined a complete rating, two investigators discussed the resulting scores, and a consensus quality score was obtained.

ResultsStudies included in the meta-analysis

Appendix C shows the data obtained in each study to describe each mediator variable for each analysed paper. Table 1 shows the basics descriptive statistics of the mediator variables. We want to highlight that the large number of patients with schizophrenia (n = 1285) and HC (n = 1279) in the included studies. Regarding the technique used to estimate connectivity networks, the most common are ALFF (20.68%) and ReHo (34.48%).

Table 1.

Descriptive statistics of the observed distributions of mediator quantitative variables.

Mediator variables  Minimum  Maximum  M  SD 
Illness duration  0.446  16.948  4.003  4.980 
Mean age schizophrenia group  13  40.050  26.650  7.732 
Mean age healthy controls  12.900  37.800  26.839  7.493 
% Men schizophrenia group  30.610  82.350  56.035  12.442 
% Men healthy controls  39.540  82.350  56.204  11.285 
Sample size schizophrenia group  14  168  44.31  37.179 
Sample size healthy controls  14  166  44.10  37.527 
PANSS total  19.240  49.120  38.019  7.899 
PANSS negative  8.410  27.490  19.601  4.200 
PANSS positive  9.530  27.530  19.297  4.801 
PANSS general  37.290  102.000  79.457  15.269 
Quality of the studies  9.50  11.00  10.760  0.385 

Note. PANSS, Positive and Negative Syndrome Scale.

Meta-analysis results

Figs. 2 and 3 present forest plots with the mean effect size and 95% CI for each study. If the effect size is negative, then the patient group shows decreased activation compared with the HC. The most significant positive effect size is 1.715, with an upper limit of 2.448. On the other hand, the highest negative effect size is -1.673, with a lower limit of -2.361. Notably, in the case of negative differences, Fryer et al. (2015) and Turner et al. (2013) show a lower CI width. Turner et al. (2013) shows the most accurate interval in the case of positive differences.

Fig. 2.

Forest plot of the positive effect size

Note. The effect size (Cohen's d; blue circle) is shown with the 95% confidence interval (lower limit in green and upper limit in yellow).

(0.22MB).
Fig. 3.

Forest plot of the negative effect size

Note. The effect size (Cohen's d; blue circle) is shown with the 95% confidence interval (lower limit in green and upper limit in yellow).

(0.25MB).

In our meta-analysis, patients with schizophrenia do not show hyperactivation compared with HC. However, they show decreased activation in the right precentral gyrus, specifically in Brodmann area (BA) 4. In addition, they show hypoactivation in the left superior temporal gyrus corresponding to BA 22. We found two clusters: one with 640 voxels and one with 150 voxels. The results are displayed in Table 2.

Table 2.

Major differences in activation between patients with schizophrenia and healthy controls in the present meta-analysis.

RegionMNI coordinateszpVoxelsBrodmann's area
Schizophrenia < healthy controlsRight precentral gyrus  50  -10  40  -5.902  .009  640  BA 4 
Left superior temporal gyrus  -58  -20  -6.128  .009  150  BA 22 

Note. MNI, Montreal Neurological Institute

Fig. 4 shows the graphical representation of the areas that are hypoactivated (visualised with the BrainNet Viewer, http://www.nitrc.org/projects/bnv/; Xia et al., 2013). Note that the size of the node is proportional to the voxels it represents – in other words, the larger the node, the more voxels there are in that area and the larger the region it represents. It is also worth noting that the blue nodes correspond to the right precentral gyrus (BA 4) and the yellow nodes refers to the left superior temporal gyrus (BA 22).

Fig. 4.

Representation of the most significant coordinates that show hypoactivation in patients with schizophrenia compared with healthy controls

Note. The right precentral Gyrus is shown in blue and the left superior temporal Gyrus is shown in yellow.

(0.19MB).
Reliability analysis

We conducted a jackknife sensitivity analysis (Table 3) to check the replicability of the results. This analysis revealed that the right precentral gyrus (with the coordinates 50, -10, 40) is replicable in all 29 datasets (each dataset with one study left out; 100% reliability), and the left superior temporal gyrus (with the coordinates -58, -20, 4) is replicable in 26/29 datasets (89.66% reliability). Therefore, the results have very high reliability.

Table 3.

Jackknife sensitivity analysis.

All studies but...  Right precentral gyrus(50, -10, 40)  Left superior temporal gyrus(-58, -20, 4) 
Alonso et al. (2017)Alonso 1  Yes  Yes 
Alonso et al. (2017)Alonso 2  Yes  Yes 
Bai et al. (2016)Bai  Yes  Yes 
Ding et al. (2019)Ding  Yes  No 
Fryer et al. (2015)Fryer  Yes  No 
Gao et al. (2015)Gao 1  Yes  Yes 
Gao et al. (2015)Gao 2  Yes  Yes 
Gao et al (2018)Gao 3  Yes  Yes 
Gou, Liu, et al. (2018)Guo  Yes  Yes 
Guo, Liu, et al. (2016)Guo 1  Yes  Yes 
Guo, Liu, Chen et al. (2017)Guo 2  Yes  Yes 
Guo, Liu, Xiao et al. (2015)Guo3  Yes  Yes 
Li et al. (2019)Li 1  Yes  Yes 
Li et al. (2019)Li 2  Yes  Yes 
Li, Lei et al. (2017)Li 3  Yes  Yes 
Liang et al. (2019)Liang 1  Yes  Yes 
Liang et al. (2019)Liang 2  Yes  Yes 
Liao et al. (2019)Liao  Yes  Yes 
Liu et al. (2016)Liu 1  Yes  Yes 
Liu et al. (2006)Liu 2  Yes  Yes 
Liu et al. (2017)Liu 3  Yes  Yes 
Ren et al. (2013)Ren  Yes  Yes 
Turner et al. (2013)Turner  Yes  No 
Wang et al. (2018)Wang  Yes  Yes 
Yan et al. (2020)Yan  Yes  Yes 
Yu et al. (2013)Yu  Yes  Yes 
Zhao et al. (2019)Zhao  Yes  Yes 
Zheng et al. (2016)Zheng  Yes  Yes 
Zhu et al. (2018)Zhu  Yes  Yes 

Note 1. In italics, an abbreviation of each study has been added for identification in the forest plots.

Note 2. Yes, the region is reported if we extract the mentioned study for the analysis; No, the region is not reported if we extract the mentioned study for the analysis

Publication bias analysis

Figs. 5 and 6 show funnel plots that graphically present publication bias. The graphs suggest that there is no publication bias because the points – which correspond to the effect sizes of each study – are distributed uniformly on both sides of ‘0’ on the x-axis (effect size). Therefore, in our case we can studies that have been published with more and less significant effects. In addition, Table 4 shows the z statistics, as well as its significance (p = .504 for the right precentral gyrus and p = .751 for the left superior temporal gyrus), which indicates that there is no bias.

Fig. 5.

Funnel plot of the effect associated with the coordinates of the right precentral Gyrus.

(0.04MB).
Fig. 6.

Funnel plot of the effect associated with the coordinates of the left superior temporal Gyrus.

(0.04MB).
Table 4.

Publication bias assessment.

Regions  z  df  p 
Right precentral gyrus  -.67  27  .504 
Left superior temporal gyrus  -.22  27  .751 

Notes. z, contrast statistic of the publication bias; df, degrees of freedom

Heterogeneity analysis

To determine the possible heterogeneity between the studies included in the present meta-analysis, we estimated Q and I2 statistics and well as the values of tau (τ), which represents the variance of the effect size distribution (Table 5). Q statistics from both positive and negative peaks are significant (p < .001). In this sense, there is heterogeneity between the studies, so it is appropriate to explore the mediator variables that could explain this phenomenon. Moreover, the degree of heterogeneity (I2) is 50.89% for positive peaks and 61.61% for negative peaks, indicating a moderate degree of heterogeneity.

Table 5.

Heterogeneity analysis of the positive and negative peaks.

Peaks  τ  Q  df  p  I2 
Positive  0.051  44.805  21  .002  50.89 
Negative  0.071  70.336  26  < .001  61.61 

Note. τ, variance of effect size distribution; Q, heterogeneity contrast statistic; df, degrees of freedom; I2, rate of heterogeneity

As mentioned above, the fact that there is heterogeneity between the studies included in the meta-analysis led us to analyse possible mediator variables that could explain the variability between the effect sizes (here considered in absolute value). Because some of the included studies have more than one significant effect, we calculated the mean effect size for each paper. We explored the following variables: the type of data analysis used (ReHo, ALFF, fALFF, etc.); sample size; age; sex; total, general, positive, and negative PANSS scores applied to the patients with schizophrenia; illness duration; and study quality.

Categorical variables

First, we used Welch's t-test to analyse the relationship between the effect size and the type of analysis (ReHo or ALFF; the rest of techniques were underrepresented and we eliminated them from this analysis). The results indicate that there is a significant relationship between the effect size and the type of analysis used, t = -2.381, df = 13.927, punivariate = .016, r = .538. In fact, the mean effect size (d) is 1.242 for ReHo studies and 0.964 for ALFF studies, which suggests that studies using ReHo can obtain a greater effect size than those using ALFF, and this effect has high intensity according to Cohen's criteria.

Quantitative variables: meta-regression analysis

We performed meta-regression to analyse whether the quantitative variables described above (Table 1) have a significant impact. Table 6 presents the minimum quadratic estimates of each meta-regression to evaluate the effect of each mediator variable on the effect size. Only sample sizes are significant predictors of the effect size heterogeneity. The negative value of the regression coefficients indicates that studies with larger sample sizes have a lower effect size. In summary, our analysis of mediator variables indicates that, in the analysed studies, the effect size is higher for ReHo estimated with smaller samples.

Table 6.

Meta-regression of the possible mediator variables and effect sizes.

Variables  Regression's coefficient (Bip 
Schizophrenia sample size  -.008  < .001 
Control sample size  -.007  < .001 
Schizophrenia sample age  .007  .419 
Control sample age  .008  .385 
Schizophrenia sample sex  .001  .993 
Control sample sex  .002  .755 
PANSS total  .004  .373 
PANSS positive  -.005  .754 
PANSS negative  .016  .316 
PANSS general  .001  .979 
Illness duration  .007  .631 
Quality of the studies  .214  .272 

Note. PANSS, Positive and Negative Syndrome Scale

Discussion

Our meta-analysis on rsFC using whole-brain studies showed hypoactivation of the left superior temporal gyrus and the right precentral gyrus in patients with schizophrenia compared with HC. In this sense, our results are partially consistent with previously published meta-analyses that employed different data analysis techniques. Li et al. (2019) analysed studies using ICA and found hypoactivation in the same brain areas in patients with schizophrenia. Xiao et al. (2017) evaluated studies using ReHo and found decreased ReHo in the left superior temporal gyrus and the right precentral gyrus in patients with schizophrenia compared with HC. Interestingly, they also suggested that unaffected relatives of patients with schizophrenia show decreased ReHo in the right superior temporal gyrus. Dong et al. (2018) included only connectivity studies using ALFF and reported reduced connectivity in the left superior temporal gyrus in patients with schizophrenia. In addition, our results are consistent with Mwansisya et al. (2017). In their systematic review, these authors concluded that patients who experience their first episode of schizophrenia present consistent alterations in the left superior temporal gyrus in both task-related activities and rest-connectivity conditions. However, it should be noted that these authors reported connectivity and task-related abnormalities in both directions (hyper- and hypoactivation) in this brain area.

The left superior temporal gyrus is part of the sensorimotor network (Li et al., 2019) and pertains to the language network (Friederici & Gerhan, 2013). This brain area has shown a progressive decrease in grey matter in longitudinal MRI studies of patients with schizophrenia (Kasai et al., 2003; Takahashi et al., 2009). Its malfunction in schizophrenia has been related to verbal auditory hallucinations (Wolf et al., 2012) and formal thought disturbances, two symptoms often experienced by patients with schizophrenia (Owen et al., 2016). Auditory hallucinations are experienced as a perception of speech without any stimulus, and brain imaging studies revealed that the left superior temporal gyrus is activated during the hallucination experienced by patients with schizophrenia (Plaze et al., 2006). Other studies suggest that in patients with chronic hallucinations, the left superior temporal gyrus shows hypoconnectivity with the left operculum, the left parietal opercular area, and the left hippocampus (Sommer et al., 2012). In their review, Alderson-Day et al. (2016) concluded that auditory hallucinations in schizophrenia are associated with alterations in the brain connectivity of the left superior temporal gyrus with several other brain structures. However, they also concluded that this symptom in schizophrenia and other disorders may be related to complex interactions between the brain areas of the sensorimotor network, including the left superior temporal gyrus, the DMN and the CCN (Alderson-Day et al., 2016). Structural abnormalities in the arcuate fasciculus have also been consistently reported in patients with schizophrenia experiencing auditory hallucinations (Geoffroy et al., 2014). This fasciculus communicates the temporal areas of the language network, including the left superior temporal gyrus, with the frontal areas of the same network (Friederici & Gerhan, 2013). Formal thought disturbances in schizophrenia are problems in the coherence and organisation of language. Various studies suggest that the presence of thought disorders and their severity in patients with schizophrenia is related to abnormal activations in the left superior temporal gyrus during language comprehension tasks (Weinstein et al., 2006) and language production tasks (Kircher et al., 2001, 2002). More recent work shows that thought disorders in schizophrenia are associated with structural abnormalities in the left superior temporal gyrus and other areas of the language network and with hyper- and hypoactivation in this same brain area during language processing and language producing tasks, such are irony comprehension, passive language perception tasks, semantic processing tasks, and free language production tasks (for a review, see Cavelti et al., 2018). Likewise, the left superior temporal gyrus consistently shows connectivity alterations in the resting state in patients with schizophrenia who experience formal thought disturbances. However, in their revision, more studies have reported resting state connectivity increases than hypoconnectivity in these patients (Cavelti et al., 2018).

We found that both the left superior frontal gyrus and the right precentral gyrus show hypoactivation in patients with schizophrenia. These brain areas are part of the praxis or gesture network mentioned in the Introduction (Andric & Small, 2012; Walther, Mittal, et al., 2020; Yang et al., 2015). In their review, Andric & Small (2012) found that the left superior temporal gyrus is an essential structure for understanding the movements of the arms and hands that accompany speech, known as co-speech gestures. Yang et al. (2015) suggest that both the left superior temporal gyrus and the right precentral gyrus are important structures to understand ‘co-speech gestures’ and gestures with meaning independent of language. Patients with schizophrenia often have problems understanding and executing gestures (Walther et al., 2015; Walther, Mittal, et al., 2020; Wüthrich et al., 2020). They also have difficulty detecting discrepancies between gestures and speech (Nagels et al., 2019). These problems tend to worsen with the course of the disease and do not improve even if patients experience clinical improvements and a reduction in the general symptoms of schizophrenia with their treatment (Wüthrich et al., 2020). The significant hypoactivation of the left superior temporal gyrus and the right precentral gyrus that we found could explain, at least in part, why patients with schizophrenia often present this type of deficit. Some neuroimaging work supports this statement, at least partially. Viher et al. (2018) showed the existence of associations between the presence of deficits in a gesture recognition task in patients with schizophrenia and a reduction in cortical thickness in structures of the praxis network of the left hemisphere, such as the inferior parietal lobe, the superior frontal gyrus, the inferior frontal gyrus, and the superior temporal gyrus.

Patients with schizophrenia also present problems in gesture production (Walther, Mittal et al., 2020). Several studies suggest that patients with schizophrenia present different types of errors in producing gestures much more frequently than HC. However, most of them do not have sufficient alterations to fulfil a diagnosis of apraxia (Walther, Mittal et al., 2020). However, another study found that around 27% of a sample of patients with schizophrenia submitted to the Test of Upper Limb Apraxia (TULIA) qualified for a diagnosis of apraxia (Walther, Alexaki, et al., 2020). Wutrich et al. (2018) showed that patients with schizophrenia who score worse than healthy participants in the TULIA present alterations in resting brain connectivity in the praxis network, including hyperconnectivity between the right precentral gyrus and the parietal lobe and hypoconnectivity between the left and right superior temporal gyrus. Likewise, Viher et al. (2018) showed that patients with schizophrenia and deficits in gestural production have a reduction in cortical thickness in structures of the praxis network, very similar to what is presented by patients with problems understanding gestures, including a reduction in the cortical thickness of the left superior temporal gyrus.

Another important aspect of our results that deserves to be highlighted is the poor significance found in brain areas other than those already mentioned. After applying the type I error rate correction, the other significant results in our whole-brain meta-analysis disappeared. Hence, our whole brain analysis does not provide sufficiently strong evidence to show other brain areas that present aberrant connectivity in patients with schizophrenia based on the previously published meta-analyses (Dong et al., 2018; Gong et al., 2020; Li et al., 2019; Xiao et al., 2017).

Regarding the effect of several variables as potential mediators of the obtained effect sizes, the sample size shows, as in previous studies, a significant effect in the sense that the effect size decreases as the sample size of patients with schizophrenia (βi = -.008, p < .001) or HC (βi = -.007, p < .001) increases. This effect is not new: it has been found in other meta-analyses that evaluated connectivity in rs-fMRI in other populations (Cañete et al., 2021). In general, it is assumed that the increase in the sample size increases a regression to the mean effect due to the reiteration of the use of the linear model in the estimation of the representative scores of each brain area considered in each registered brain volume. Concerning the effect associated with the estimation technique that indicates that ReHo presents larger effect sizes than ALFF, this effect is because ReHo is based on an estimation centered on the centrality of each ROI. In contrast, ALFF relies on studying low-frequency fractional amplitude. ALFF, therefore, is a much more restrictive technique than ReHo, so it is more conservative in terms of significance (Yue et al., 2020).

None of the other variables we considered as potential mediators have a significant effect. Of note, we found no effect of the illness duration on the rsFC of our patients. In contrast, Dong et al. (2018) found several significant relationships with disease duration in their effect estimations. The connectivity within the DMN of their patients with schizophrenia gradually decreases with longer disease duration, and the negative connectivity between the DMN and VAN increases across illness duration. We also did not find an effect of the PANSS scores as a moderator variable. Gong et al. (2018) found that a higher PANSS total score correlates with increases in ALFF in the right putamen and the superior occipital gyrus and smaller decreases in the inferior temporal gyrus and anterior cingulate cortex in patients experiencing their first schizophrenic episode. These authors also found that patients with chronic schizophrenia and a higher PANSS total score exhibit smaller decreases in ALFF in the right inferior frontal gyrus, the anterior cingulate cortex, and the left inferior temporal gyrus.

There are some limitations to this meta-analysis. We did not include studies that did not report the three-dimensional activation coordinates. We also excluded studies that did not report the corresponding statistical contrasts associated with the three-dimensional activation coordinates. In addition, we did not consider either the different subtypes of schizophrenia or the disease duration. Some previously published meta-analyses showed different connectivity effects related to this last variable when assessed qualitatively (Gong et al., 2020) or quantitatively (Dong et al., 2018). In addition, some analysis techniques used by the studies we included were underrepresented and could not be considered when evaluating whether they could predict the effect size. Finally, several original papers in our meta-analysis did not report the PANSS scores, which impeded proper analysis of these scores as potential moderator variables of the obtained effect sizes.

Our work also has some strengths. As far as we know, this is the first meta-analysis of brain rsFC in patients with schizophrenia using a whole-brain analysis approach. We must note that two independent researchers carried out a thorough search of the literature by following the PRISMA guidelines. In addition, we did not find any publication bias. We also analysed the quality of the studies: all of the included studies in our meta-analysis are high quality. In addition, the jackknife sensitivity analysis showed that our results are replicable in almost all included studies in our meta-analysis, demonstrating that our results are highly reliable.

In conclusion, our results showed that patients with schizophrenia show hypoactivation of left superior temporal gyrus and the right precentral gyrus. In other studies, these changes been associated with some clinical symptoms usually present in patients with schizophrenia, including the presence of auditory verbal hallucinations, formal thought disorder, and the comprehension and production of gestures.

Acknowledgments

This study was funded by the Ministry of Science, Innovation and State Research Agency (Project code: PGC2018-095829-B-I00).

Appendix A

Keyword Combinations Used in the Search

SCOPUS

Title: schizophreni*

AND

Title/Abstract/Keywords: fMRI OR ‘functional magnetic resonance imag*’ OR ‘functional neuroimag*’ OR ‘functional MRI’ OR ‘functional connectivity’

AND

Title/Abstract/Keywords: ‘resting state’ OR rest

Web Of Science

Title: schizophreni*

AND

Topic: fMRI OR ‘functional magnetic resonance imag*’ OR ‘functional neuroimag*’ OR ‘functional MRI’ OR ‘functional connectivity’

AND

Topic: ‘resting state’ OR rest

PubMed (new version)

Title: schizophreni*

AND

Title/Abstract: fMRI OR ‘functional magnetic resonance imag*’ OR ‘functional neuroimag*’ OR ‘functional MRI’ OR ‘functional connectivity’

AND

Title/Abstract: ‘resting state’ OR rest

Appendix B
Quality Assessment Scale

The 11-point checklist concentrates on both the clinical and neuroimaging methodology of each study and mainly assesses the quality from three aspects: subjects, methods for image acquisition and analysis, and results and conclusions. Each item is matched with the study and then assigned a score of 1 (fully satisfied), 0.5 (partially satisfied), or 0 (otherwise).

Category 1: Subjects

  • 1

    Patients were evaluated prospectively, specific diagnostic criteria were applied, and demographic data were reported.

  • 2

    Healthy comparison subjects were evaluated prospectively; psychiatric and medical illnesses were excluded.

  • 3

    Important variables (e.g., age, sex, illness duration, onset time, medication status, comorbidity, severity of illness) were checked, either by stratification or statistically.

  • 4

    Sample size per group > 10.

Category 2: Methods for image acquisition and analysis

  • 1

    Magnet strength ≥ 1.5 Tesla.

  • 2

    For resting-state functional magnetic resonance imaging studies, whole brain analysis was automated with no a priori regional selection.

  • 3

    Coordinates reported in a standard space.

  • 4

    Processing of the imaging technique was described clearly enough to be reproduced.

  • 5

    Measurements were described clearly enough to be reproduced.

Category 3: Results and conclusions

  • 1

    Statistical parameters were provided for significant and important non-significant differences.

  • 2

    Conclusions were consistent with the results obtained, and the limitations were discussed.

Appendix C

Demographic and clinical characteristics of the included studies

StudySample sizeMean age (years)% of menPANSS scoreIllness duration (months)Type of analysis
Schizophrenia  Controls  Schizophrenia  Controls  Schizophrenia  Controls  Total  Positive  Negative  General 
Alonso1  19  20  40.05  37.75  68.42  65    17.89  21.47  34.22  16.11  ALFF/fALFF 
Alonso2  14  20  36.43  37.75  57.14  65    11.43  14.36  34.22  8.00  ALFF/fALFF 
Bai  17  17  26.00  28.71  82.35  82.35  82.06  15.29  24.88    3.3575  ReHo 
Ding  44  44  23.45  23.55  63.63  52.27  90.70  22.48  22.50  45.73  1.86  GFC 
Fryer  168  166  39.04  37.80  71.43  75.90  58.78  15.55  14.49  38.73    fALFF 
Gao1  14  14  33.2  34.9  64.29  64.28  74.10  16.40  22.60  30.80  9.20  ReHo 
Gao2  17  29  31.24  32.73  58.82  55.17  97.76  27.53  21.05  49.12  14.00  ReHo 
Gao3  17  29  36.82  32.73  52.94  55.17  37.29  9.53  8.41  19.24  7.88  ReHo 
Gou  28  21  23.9  28.8  57.14  66.67  85.70  17.80  21.00  38.90  1.2583  ReHo 
Guo1  49  50  22.69  23.48  61.22  46.00  91.31  22.27  22.82    1.8708  LSFCS 
Guo2  17  24  32.12  30.67  47.06  45.83  88.06  23.00  20.24  44.82  1.0025  LSFCS 
Guo3  17  24  33.12  30.67  47.06  45.83  88.06  23.00  20.24  44.82  1.0025  VMHC 
Li1  41  42  23.32  23.29  56.09  57.14  99.38  22.15  27.49  46.63  1.6792  ALFF 
Li2  42  42  22.86  23.29  59.52  57.14  86.22  25.38  16.48  46.63  1.6525  ALFF 
Li3  32  32  30.94  31.37  50.00  65.63  77.38  20.00  20.59  36.78  0.7425  GFC 
Liang1  30  30  13.00  12.9  36.67  46.67  74.50        0.4583  ALFF 
Liang2  21  30  13.2  12.9  38.09  46.67  76.40        0.45  ALFF 
Liao  49  43  30.22  33.03  30.61  39.54  62.69  12.72  19.77  29.85  6.0145  VMHC 
Liu1  18  18  23.67  24.44  50.00  50.00  80.39          ReHo 
Liu2  27  27  25.44  27.44  55.56  66.67  85.78  21.56  23.15  41.30  1.5267  ReHo/ALFF 
Liu3  48  31  15.79  15.42  43.75  45.16  75.10  21.50  17.92  34.25  0.4458  ReHo 
Ren  100  100  24.30  24.39  41.00  41.00  97.00  25.11  18.84  47.63  0.5208  ALFF 
Turner  147  159  37.70  37.15  75.50  71.69  57.16  14.92  14.32  27.91  16.9483  ALFF/fALFF 
Wang  48  31  15.79  15.42  43.75  45.14  75.10  21.50  17.92  34.25  0.4458  LSFCS 
Yan  69  74  24.22  26.27  72.46  60.81  84.19  24.42  17.58  42.19  1.1450  ReHo 
Yu  69  62  31.7  29.9  49.28    52.90  12.10  13.40  27.40  7.10  ReHo 
Zhao  44  26  23.7  22.6  70.46  65.39  102.00  15.30  24.70    1.00  ReHo 
Zheng  35  30  15.5  15.43  57.14  43.33  74.62  20.42  20.91  33.28  0.55  ALFF 
Zhu  44  44  23.45  23.55  63.64  52.27  90.70  22.48  22.50  45.73  1.8617  PAS 

Note. ALFF, amplitude of low-frequency fluctuation; fALFF, fractional amplitude of low-frequency fluctuation; GFC, global function connectivity; PAS, parameter of asymmetry; PANSS, Positive and Negative Syndrome Scale; LSFCS, long short functional connectivity; ReHo, regional homogeneity; VMHC, voxel-mirrored homotopic connectivity.

References
[Albajes-Eizagirre et al., 2021]
A. Albajes-Eizagirre, A. Solanes, E. Vieta, R. Salvador, E. Pomarol, J. Radua.
Voxel-based meta-analysis via permutation of subject images (PSI): Theory and implementation for SDM.
NeuroImage, 186 (2021), pp. 174-184
[Alderson-Day et al., 2016]
B. Alderson-Day, K. Diederen, C. Fernyhough, J.M. Ford, G. Horga, D.S. Margulies, R. Jardri.
Auditory hallucinations and the brain's resting-state networks: Findings and methodological observations.
Schizophrenia Bulletin, 42 (2016), pp. 1110-1123
[Alonso-Solís et al., 2017]
A. Alonso-Solís, Y. Vives-Gilabert, M.J. Portella, M. Rabella, E.M. Grasa, A. Roldán, A. Keymer-Gausset, C. Molins, F. Núñez-Marín, B. Gómez-Ansón, E. Álvarez, I. Corripio.
Altered amplitude of low frequency fluctuations in schizophrenia patients with persistent auditory verbal hallucinations.
Schizophrenia Research, 189 (2017), pp. 97-103
[Andric and Small, 2012]
M. Andric, S.L. Small.
Gesture's neural language.
Frontiers in psychology, 3 (2012), pp. 99
[Bai et al., 2016]
Y. Bai, W. Wang, J. Xu, F. Zhang, H. Yu, C. Luo, L. Wang, X. Chen, B. Shan, L. Xu, X. Xu, Y. Cheng.
Altered resting-state regional homogeneity after 13 weeks of paliperidone injection treatment in schizophrenia patients.
Psychiatry Research: Neuroimaging, 258 (2016), pp. 37-43
[Bluhm et al., 2007]
R.L. Bluhm, J. Miller, R.A. Lanius, E.A. Osuch, K. Boksman, R.W.J. Neufeld, P. Williamson.
Spontaneous low-frequency fluctuations in the BOLD signal in schizophrenic patients: anomalies in the default network.
Schizophrenia Bulletin, 33 (2007), pp. 1004-1012
[Cavelti et al., 2018]
Cavelti, M., Kircher, T., Nagels, A., Strik, W., & Homan, P. (2018). Is formal thought disorder in schizophrenia related to structural and functional aberrations in the language network? A systematic review of neuroimaging findings. Schizophrenia Research, 199, 2–16. 10.1016/j.schres.2018.02.051.
[Cañete et al., 2021]
C. Cañete, M. Carbó, M. Peró, J. Guardia.
Task-related brain activation fMRI in intellectual disability population: A metanalytic study.
Brain Connectivity, (2021), pp. 788-798
[Chen et al., 2015]
Z.Q. Chen, M.Y. Du, Y.J. Zhao, X.Q. Huang, J. Li, S. Lui, J. Hu, H. Sun, J. Liu, G.J. Kemp, Q.Y Gong.
Voxel-wise meta-analyses of brain blood flow and local synchrony abnormalities in medication-free patients with major depressive disorder.
Journal of Psychiatry & Neuroscience, 40 (2015), pp. 401-411
[Ding et al., 2019]
Y. Ding, Y. Ou, Q. Su, P. Pan, X. Shan, J. Chen, F. Liu, Z. Zhang, J. Zhao, W. Guo.
Enhanced global-brain functional connectivity in the left superior frontal gyrus as a possible endophenotype for schizophrenia.
Frontiers in Neuroscience, 13 (2019), pp. 145
[Dong et al., 2018]
D. Dong, Y. Wang, X. Chang, C. Luo, D. Yao.
Dysfunction of large-scale brain networks in schizophrenia: a meta-analysis of resting-state functional connectivity.
Schizophrenia Bulletin, 44 (2018), pp. 168-181
[Fryer et al., 2015]
S.L. Fryer, B.J. Roach, J.M. Ford, J.A. Turner, T.G. Van Erp, J. Voyvodic, A. Preda, A. Belger, J. Bustillo, D. O'Leary, B.A. Mueller, K.O. Lim, S.C. McEwen, V.D. Calhoun, M. Diaz, G. Glover, D. Greve, C.G. Wible, J. Vaidya, D.H. Mathalon.
Relating intrinsic low-frequency BOLD cortical oscillations to cognition in schizophrenia.
Neuropsychopharmacology, 40 (2015), pp. 2705-2714
[Friederici and Gierhan, 2013]
A.D. Friederici, S.M. Gierhan.
The language network.
Current Opinion in Neurobiology, 23 (2013), pp. 250-254
[Friston and Frith, 1995]
K.J. Friston, C.D. Frith.
Schizophrenia: A disconnection syndrome.
Clinical Neuroscience, 3 (1995), pp. 89-97
[Forlim et al., 2020]
C.G. Forlim, L. Klock, J. Bächle, L. Stoll, P. Giemsa, M. Fuchs, N. Shoofs, C. Montag, J. Gallinat, S. Kühn.
Reduced resting-state connectivity in the precuneus is correlated with apathy in patients with schizophrenia.
Scientific Reports, 10 (2020), pp. 2616
[Fornito et al., 2011]
A. Fornito, J. Yoon, A. Zalesky, E.T. Bullmore, C.S. Carter.
General and specific functional connectivity disturbances in first-episode schizophrenia during cognitive control performance.
Biological Psychiatry, 70 (2011), pp. 64-72
[Gao et al., 2015]
B. Gao, Y. Wang, W. Liu, Z. Chen, H. Zhou, J. Yang, Z. Cohen, Y. Zhu, Y. Zang.
Spontaneous activity associated with delusions of schizophrenia in the left medial superior frontal gyrus: A resting-state fMRI study.
[Gao et al., 2018]
S. Gao, S. Lu, X. Shi, Y. Ming, C. Xiao, J. Sun, H. Yao, X. Xu.
Distinguishing between treatment-resistant and non-treatment-resistant schizophrenia using regional homogeneity.
Frontiers in Psychiatry, 9 (2018), pp. 282
[Geoffroy et al., 2014]
P.A. Geoffroy, J. Houenou, A. Duhamel, A. Amad, A.D. De Weijer, B. Ćurčić-Blake, R. Jardri.
The arcuate fasciculus in auditory-verbal hallucinations: A meta-analysis of diffusion-tensor-imaging studies.
Schizophrenia Research, 159 (2014), pp. 234-237
[Gong et al., 2020]
J. Gong, J. Wang, X. Luo, G. Chen, H. Huang, R. Huang, L. Huang, Y. Wang.
Abnormalities of intrinsic regional brain activity in first-episode and chronic schizophrenia: A meta-analysis of resting-state functional MRI.
Journal of Psychiatry & Neuroscience, 45 (2020), pp. 55-68
[Gou et al., 2018]
N. Gou, Z. Liu, L. Palaniyappan, M. Li, Y. Pan, X. Chen, H. Tao, G. Wu, X. Ouyang, Z. Wang, T. Dou, Z. Xue, W. Pu.
Effects of DISC1 polymorphisms on resting-state spontaneous neuronal activity in the early-stage of schizophrenia.
Frontiers in Psychiatry, 9 (2018), pp. 137
[Guo et al., 2017]
W. Guo, F. Liu, J. Chen, R. Wu, L. Li, Z. Zhang, H. Chen, J. Zhao.
Treatment effects of olanzapine on homotopic connectivity in drug-free schizophrenia at rest.
The World Journal of Biological Psychiatry, 19 (2017), pp. S106-S114
[Guo et al., 2016]
W. Guo, F. Liu, J. Chen, R. Wu, L. Li, Z. Zhang, J. Zhao.
Olanzapine modulation of long-and short-range functional connectivity in the resting brain in a sample of patients with schizophrenia.
European Neuropsychopharmacology, 27 (2016), pp. 48-58
[Guo et al., 2015]
W. Guo, F. Liu, C. Xiao, J. Liu, M. Yu, Z. Zhang, J. Zhang, J. Zhao.
Increased short-range and long-range functional connectivity in first-episode, medication-naive schizophrenia at rest.
Schizophrenia Research, 166 (2015), pp. 144-150
[He et al., 2013]
Z. He, W. Deng, M. Li, Z. Chen, L. Jiang, Q. Wang, T. Li.
Aberrant intrinsic brain activity and cognitive deficit in first-episode treatment-naive patients with schizophrenia.
Psychological Medicine, 43 (2013), pp. 769-780
[Horne et al., 2021]
C.M. Horne, L.D. Vanes, T. Verneuil, E. Mouchlianitis, T. Szentgyorgyi, B. Averbeck, S.S. Shergill.
Cognitive control network connectivity differentially disrupted in treatment resistant schizophrenia.
NeuroImage: Clinical, 30 (2021),
[Huang et al., 2019]
H. Huang, Z. Botao, Y. Jiang, Y. Tang, T. Zhang, X. Tang, J. Wang.
Aberrant resting-state functional connectivity of salience network in first-episode schizophrenia.
Brain Imaging and Behavior, 3 (2019), pp. 1-11
[Kasai et al., 2003]
K. Kasai, M.E. Shenton, D.F. Salisbury, Y. Hirayasu, C.U. Lee, A.A. Ciszewski, R.W. McCarley.
Progressive decrease of left superior temporal gyrus gray matter volume in patients with first-episode schizophrenia.
American Journal of Psychiatry, 160 (2003), pp. 156-164
[Kircher et al., 2001]
T.T. Kircher, P.F. Liddle, M.J. Brammer, S.C. Williams, R.M. Murray, P.K. McGuire.
Neural correlates of formal thought disorder in schizophrenia: Preliminary findings from a functional magnetic resonance imaging study.
Archives of General Psychiatry, 58 (2001), pp. 769-774
[Kircher et al., 2002]
T.T. Kircher, P.F. Liddle, M.J. Brammer, S.C.R. Williams, R.M. Murray, P.K. McGuire.
Reversed lateralization of temporal activation during speech production in thought disordered patients with schizophrenia.
Psychological Medicine, 32 (2002), pp. 439-449
[Kühn and Gallinat, 2013]
S. Kühn, J. Gallinat.
Resting-state brain activity in schizophrenia and major depression: a quantitative meta-analysis.
Schizophrenia Bulletin, 39 (2013), pp. 358-365
[Larivière et al., 2017]
S. Larivière, K.M. Lavigne, T.S. Woodward, P. Gerretsen, A. Graff-Guerrero, M. Menon.
Altered functional connectivity in brain networks underlying self-referential processing in delusions of reference in schizophrenia.
Psychiatry Research: Neuroimaging, 263 (2017), pp. 32-43
[Li et al., 2019]
H. Li, Y. Ou, F. Liu, J. Chen, J. Zhao, W. Guo, X. Fan.
Reduced connectivity in anterior cingulate cortex as an early predictor for treatment response in drug-naive, first-episode schizophrenia: A global-brain functional connectivity analysis.
Schizophrenia Research, 215 (2019), pp. 337-343
[Li et al., 2019]
S. Li, N. Hu, W. Zhang, B. Tao, J. Dai, Y. Gong, Y. Tan, D. Cai, S. Lui.
Dysconnectivity of multiple brain networks in schizophrenia: A meta-analysis of resting-state functional connectivity.
Frontiers in Psychiatry, 10 (2019), pp. 482
[Li et al., 2017]
Z. Li, W. Lei, W. Deng, Z. Zheng, M. Li, X. Ma, Q. Wang, X. Huang, N. Li, D.A. Collier, Q. Gong, T. Li.
Aberrant spontaneous neural activity and correlation with evoked-brain potentials in first-episode, treatment-naïve patients with deficit and non-deficit schizophrenia.
Psychiatry Research: Neuroimaging, 261 (2017), pp. 9-19
[Liang et al., 2019]
Y. Liang, R. Shao, Z. Zhang, X. Li, L. Zhou, S. Guo.
Amplitude of low-frequency fluctuations in childhood-onset schizophrenia with or without obsessive-compulsive symptoms: A resting-state functional magnetic resonance imaging study.
Archives of Medical Science, 15 (2019), pp. 126
[Liao et al., 2019]
W. Liao, S. Yang, J. Li, Y.S. Fan, X. Duan, Q. Cui, H. Chen.
Nicotine in action: Cigarette smoking modulated homotopic functional connectivity in schizophrenia.
Brain Imaging and Behavior, 13 (2019), pp. 1612-1623
[Liu et al., 2016]
C. Liu, Z. Xue, L. Palaniyappan, L. Zhou, H. Liu, C. Qi, G. Wu, T.E. Mwansisya, H. Tao, X. Chen, X. Huang, Z. Liu, W. Pu.
Abnormally increased and incoherent resting-state activity is shared between patients with schizophrenia and their unaffected siblings.
Schizophrenia Research, 171 (2016), pp. 158-165
[Liu et al., 2006]
H. Liu, Z. Liu, M. Liang, Y. Hao, L. Tan, F. Kuang, Y. Yi, L. Xu, T. Jiang.
Decreased regional homogeneity in schizophrenia: A resting state functional magnetic resonance imaging study.
[Liu et al., 2017]
Y. Liu, Y. Zhang, L. Lv, R. Wu, J. Zhao, W. Guo.
Abnormal neural activity as a potential biomarker for drug-naive first-episode adolescent-onset schizophrenia with coherence regional homogeneity and support vector machine analyses.
Schizophrenia Research, 192 (2017), pp. 408-415
[Menon et al., 2022]
V. Menon, L. Palaniyappan, K. Supekar.
Integrative brain network and salience models of psychopathology and cognitive dysfunction in schizophrenia.
[Moran et al., 2013]
L.V. Moran, M.A. Tagamets, H. Sampath, A. O'Donnell, E.A. Stein, P. Kochunov, L.E. Hong.
Disruption of anterior insula modulation of large-scale brain networks in schizophrenia.
Biological Psychiatry, 74 (2013), pp. 467-474
[Müller et al., 2018]
V.I. Müller, E.C. Cieslik, A.R. Laird, P.T. Fox, J. Radua, D. Mataix-Cols, S.B. Eickhoff.
Ten simple rules for neuroimaging meta-analysis.
Neuroscience & Biobehavioural Reviews, 84 (2018), pp. 151-161
[Mwansisya et al., 2017]
T.E. Mwansisya, A. Hu, Y. Li, X. Chen, G. Wu, X. Huang, Z. Liu.
Task and resting-state fMRI studies in first-episode schizophrenia: A systematic review.
Schizophrenia Research, 189 (2017), pp. 9-18
[Nagels et al., 2019]
A. Nagels, T. Kircher, M. Grosvald, M. Steines, B. Straube.
Evidence for gesture-speech mismatch detection impairments in schizophrenia.
Psychiatry Research, 273 (2019), pp. 15-21
[Owen et al., 2016]
M.J. Owen, A. Sawa, P.B. Mortensen.
Schizophrenia.
The Lancet, 388 (2016), pp. 86-97
[Palaniyappan and Liddle, 2012]
L. Palaniyappan, P.F. Liddle.
Does the salience network play a cardinal role in psychosis? An emerging hypothesis of insular dysfunction.
Journal of Psychiatry and Neuroscience, 37 (2012), pp. 17-27
[Plaze et al., 2006]
M. Plaze, D. Bartrés-Faz, J.L. Martinot, D. Januel, F. Bellivier, R. De Beaurepaire, M.L. Paillère-Martinot.
Left superior temporal gyrus activation during sentence perception negatively correlates with auditory hallucination severity in schizophrenia patients.
Schizophrenia Research, 87 (2006), pp. 109-115
[Radua and Mataix, 2012]
J. Radua, D. Mataix.
Meta-analytic methods for neuroimaging data explained.
Biological Mood Anxiety Disorders, 2 (2012), pp. 6
[Ray et al., 2017]
K.L. Ray, T.A. Lesh, A.M. Howell, T.P. Salo, J.D. Ragland, A.W. MacDonald, C.S. Carter.
Functional network changes and cognitive control in schizophrenia.
NeuroImage: Clinical, 15 (2017), pp. 161-170
[Ren et al., 2013]
W. Ren, S. Lui, W. Deng, F. Li, M. Li, X. Huang, Y. Wang, T. Li, J.A. Sweeney, Q. Gong.
Anatomical and functional brain abnormalities in drug-naive first-episode schizophrenia.
American Journal of Psychiatry, 170 (2013), pp. 1308-1316
[Sommer et al., 2012]
I.E. Sommer, M. Clos, A.L. Meijering, K.M. Diederen, S.B. Eickhoff.
Resting state functional connectivity in patients with chronic hallucinations.
[Shepherd et al., 2012]
A.M. Shepherd, S.L. Matheson, K.R. Laurens, M.J. Green.
Systematic meta- analysis of insula volume in schizophrenia.
Biological Psychiatry, 72 (2012), pp. 775-784
[Skudlarski et al., 2010]
P. Skudlarski, K. Jagannathan, K. Anderson, M.C. Stevens, V.D. Calhoun, B.A. Skudlarska, G. Pearlson.
Brain connectivity is not only lower but different in schizophrenia: A combined anatomical and functional approach.
Biological Psychiatry, 68 (2010), pp. 61-69
[Takahashi et al., 2009]
T. Takahashi, S.J. Wood, A.R. Yung, B. Soulsby, P.D. McGorry, M. Suzuki, C. Pantelis.
Progressive gray matter reduction of the superior temporal gyrus during transition to psychosis.
Archives of General Psychiatry, 66 (2009), pp. 366-376
[Turner et al., 2013]
J.A. Turner, E. Damaraju, T.G. Van Erp, D.H. Mathalon, J.M. Ford, J. Voyvodic, B.A. Mueller, A. Belger, J. Bustillo, S. McEwen, S.G. Potkin, V.D. Calhoun.
A multi-site resting state fMRI study on the amplitude of low frequency fluctuations in schizophrenia.
Frontiers in Neuroscience, 7 (2013), pp. 137
[Van Den Heuvel and Fornito, 2014]
M.P. Van Den Heuvel, A Fornito.
Brain networks in schizophrenia.
Neuropsychology Review, 24 (2014), pp. 32-48
[Viher et al., 2018]
P.V. Viher, K. Stegmayer, M. Kubicki, S. Karmacharya, A.E. Lyall, A. Federspiel, S. Walther.
The cortical signature of impaired gesturing: findings from schizophrenia.
NeuroImage: Clinical, 17 (2018), pp. 213-221
[Walther et al., 2020]
S. Walther, D.D. Alexaki, K.D.L. Stegmayer, T. Vanbellingen, S. Bohlhalter.
Conceptual disorganization impairs hand gesture performance in schizophrenia.
Schizophrenia Research, 215 (2020), pp. 467-468
[Walther et al., 2020]
S. Walther, V.A. Mittal, K. Stegmayer, S. Bohlhalter.
Gesture deficits and apraxia in schizophrenia.
[Walther et al., 2015]
S. Walther, K. Stegmayer, J. Sulzbacher, T. Vanbellingen, R. Müri, W. Strik, S. Bohlhalter.
Nonverbal social communication and gesture control in schizophrenia.
Schizophrenia Bulletin, 41 (2015), pp. 338-345
[Wang et al., 2018]
S. Wang, Y. Zhan, Y. Zhang, L. Lyu, H. Lyu, G. Wang, R. Wu, J. Zhao, W. Guo.
Abnormal long-and short-range functional connectivity in adolescent-onset schizophrenia patients: A resting-state fMRI study.
Progress in Neuro-Psychopharmacology and Biological Psychiatry, 81 (2018), pp. 445-451
[Weinstein et al., 2006]
S. Weinstein, J.F. Werker, A. Vouloumanos, T.S. Woodward, E.T. Ngan.
Do you hear what I hear? Neural correlates of thought disorder during listening to speech in schizophrenia.
Schizophrenia Research, 86 (2006), pp. 130-137
[Wolf et al., 2012]
N. Wolf, G. Grön, F. Sambataro, N. Vasic, K. Frasch, M. Schmid.
Magnetic resonance perfusion imaging of auditory verbal hallucinations in patients with schizophrenia.
Schizophrenia Research, 134 (2012), pp. 285-287
[Woodruff et al., 1997]
P.W. Woodruff, I.C. Wright, E.T. Bullmore, M. Brammer, R.J. Howard, S.C. Williams, R.M. Murray.
Auditory hallucinations and the temporal cortical response to speech in schizophrenia: A functional magnetic resonance imaging study.
American Journal of Psychiatry, 154 (1997), pp. 1676-1682
[Woodward et al., 2011]
N.D. Woodward, B. Rogers, S. Heckers.
Functional resting-state networks are differentially affected in schizophrenia.
Schizophrenia Research, 130 (2011), pp. 86-93
[Wüthrich et al., 2020]
F. Wüthrich, A. Pavlidou, K. Stegmayer, S. Eisenhardt, J. Moor, L. Schäppi, S. Walther.
Nonverbal communication remains untouched: No beneficial effect of symptomatic improvement on poor gesture performance in schizophrenia.
Schizophrenia Research, 223 (2020), pp. 258-264
[Xia et al., 2013]
M. Xia, J. Wang, Y. He.
BrainNet Viewer: A network visualization tool for human brain connectomics.
[Xiao et al., 2017]
B. Xiao, S. Wang, J. Liu, T. Meng, Y. He, X. Luo.
Abnormalities of localized connectivity in schizophrenia patients and their unaffected relatives: a meta-analysis of resting-state functional magnetic resonance imaging studies.
Neuropsychiatric Disease and Treatment, 13 (2017), pp. 467
[Yan et al., 2020]
W. Yan, R. Zhang, M. Zhou, S. Lu, W. Li, S. Xie, N. Zhang.
Relationships between abnormal neural activities and cognitive impairments in patients with drug-naive first-episode schizophrenia.
BMC Psychiatry, 20 (2020), pp. 283
[Yang et al., 2015]
J. Yang, M. Andric, M.M. Mathew.
The neural basis of hand gesture comprehension: A meta-analysis of functional magnetic resonance imaging studies.
Neuroscience & Biobehavioral Reviews, 57 (2015), pp. 88-104
[Yu et al., 2013]
R. Yu, M.H. Hsieh, H.L.S. Wang, C.M. Liu, C.C. Liu, T.J. Hwang, Y.L. Chien, H.G. Hwu, W.Y.I. Tseng.
Frequency dependent alterations in regional homogeneity of baseline brain activity in schizophrenia.
[Yue et al., 2020]
Y. Yue, Y. Jiang, T. Shen, J. Pi, H. Lai, B. Zhang.
ALFF and ReHo mapping reveals different functional patterns in early- and late-onset Parkinson's disease.
Frontiers in Neuroscience, 25 (2020),
[Zhang et al., 2020]
T. Zhang, C. Li, P. Li, Y. Peng, X. Kang, C. Jiang, P. Xu.
Separated channel attention convolutional neural network (SC-CNN-attention) to identify ADHD in multi-site rs-fMRI dataset.
Entropy, 22 (2020), pp. 893
[Zhao et al., 2019]
X. Zhao, J. Yao, Y. Lv, X. Zhang, C. Han, L. Chen, F. Ren, Z. Jin, Y. Li, Y. Sui.
Abnormalities of regional homogeneity and its correlation with clinical symptoms in naïve patients with first-episode schizophrenia.
Brain Imaging and Behavior, 13 (2019), pp. 503-513
[Zheng et al., 2016]
J. Zheng, Y. Zhang, X. Guo, X. Duan, J. Zhang, J. Zhao, H. Chen.
Disrupted amplitude of low-frequency fluctuations in antipsychotic-naive adolescents with early-onset schizophrenia.
Psychiatry Research: Neuroimaging, 249 (2016), pp. 20-26
[Zhu et al., 2018]
F. Zhu, F. Liu, W. Guo, J. Chen, Q. Su, Z. Zhang, H. Li, X. Fan, J. Zhao.
Disrupted asymmetry of inter-and intra-hemispheric functional connectivity in patients with drug-naive, first-episode schizophrenia and their unaffected siblings.
EBioMedicine, 36 (2018), pp. 429-435
Copyright © 2023. The Authors
Descargar PDF
Opciones de artículo
Quizás le interese:
10.1016/j.ijchp.2022.100317
No mostrar más