Published online Dec 19, 2021. doi: 10.5498/wjp.v11.i12.1274
Peer-review started: April 14, 2021
First decision: June 5, 2021
Revised: June 15, 2021
Accepted: August 31, 2021
Article in press: August 31, 2021
Published online: December 19, 2021
The present resting-state functional magnetic resonance imaging (rs-fMRI) study was conducted in two groups of patients – schizophrenia (SCH) and individuals with mood disorders with the depressive syndrome (Ds) – to delineate the effective connectivity patterns at rest with the prior hypothesis that the salience network (SN) in SCH must have a fundamentally impaired connectivity, which prevents the switching between anticorrelated default mode network (DMN) and central executive network (CEN), thereby interfering with their basic functions and that this disruption may serve as neuroimaging biomarker to distinguish between the two groups of patients.
Our motivation to conduct such a comparative study comes from the lack of biological validity of available diagnostic tools, which ultimately leads to inaccurate diagnosis or high rates of comorbidity, and therefore an inadequate choice of treatment for psychotic and affective disorders.
By proving neurobiological markers to distinguish between SCH and mood disorders, we aimed to expand knowledge about their etiology and incorporate it into clinical practice, ultimately optimizing diagnosis and prognosis, and thus choosing the right treatment for these severe mental illnesses.
The methods used include rs-fMRI and subsequent dynamic causal modeling (spDCM) to determine the direction and strength of connections to and from various nodes in the DMN, SN and CEN. The positive and negative syndrome scale was chosen for the assessment of the SCH group, and the severity of the Ds was assessed using the Montgomery–Åsberg Depression Rating Scale. The SPM 12 software running on MATLAB R2020b for Windows was used to perform data analysis. First level resting-state analysis was conducted using a general linear model. Regions of interest were predefined based on their involvement in the SN and the DNM. Furthermore, using the parametric empirical bayes method introduced in SPM12, the individual spDCM models were jointly estimated. Finally, the estimated spDCM models were used to extract connectivity strengths (A-matrix) for further statistical analysis in SPSS.
The coupling strengths of the connection from the precuneus (Pc) to the prefrontal cortex and from the anterior insula (aI) to the Pc, both inhibitory connections were present in the Ds group but absent in the SCH group. In the SCH patients, a significant excitatory connection from the dorsal part of the anterior cingulate cortex to the aI was present which was absent in the Ds study group.
We managed to deliver evidence that despite the clinical overlaps, there are objective neuroimaging signatures of disease that can fundamentally distinguish SCH from mood disorders. The resting state of aberrant salience and proximal salience observed in the schizophrenic group has the potential to explain not only the psychotic symptoms, such as hallucinations and delusions, but also gives insight into the formation of the unique for SCH behavioral and thought disorganization.
We suggest that our findings could help both in the biological understanding of the etiology of SCH and mood disorders in the development and improvement of the therapeutic approach. We visualize a future where translational neuroscience would ultimately integrate psychopathology, psychopharmacology, instrumental methods, and even neurosurgical techniques to restore brain imbalances by modulating the altered connectivity in the brains of people suffering from SCH and mood disorders.