Unraveling the pathophysiology of schizophrenia: insights from structural magnetic resonance imaging studies

Mohammed Jajere Adamu; Li Qiang; Charles Okanda Nyatega; Ayesha Younis; Halima Bello Kawuwa; Adamu Halilu Jabire; Sani Saminu

doi:10.3389/fpsyt.2023.1188603

Unraveling the pathophysiology of schizophrenia: insights from structural magnetic resonance imaging studies

Front Psychiatry. 2023 May 19:14:1188603. doi: 10.3389/fpsyt.2023.1188603. eCollection 2023.

Authors

Mohammed Jajere Adamu^{1

2}, Li Qiang¹, Charles Okanda Nyatega^{3

4}, Ayesha Younis¹, Halima Bello Kawuwa⁵, Adamu Halilu Jabire⁶, Sani Saminu⁷

Affiliations

¹ Department of Electronic Science and Technology, School of Microelectronics, Tianjin University, Tianjin, China.
² Department of Computer Science, Yobe State University, Damaturu, Nigeria.
³ Department of Information and Communication Engineering, School of Electrical and Information Engineering, Tianjin University, Tianjin, China.
⁴ Department of Electronics and Telecommunication Engineering, Mbeya University of Science and Technology, Mbeya, Tanzania.
⁵ Department of Biomedical Engineering and Scientific Instruments, School of Precision Instruments and Optoelectronics Engineering, Tianjin University, Tianjin, China.
⁶ Department of Electrical and Electronics Engineering, Taraba State University, Jalingo, Nigeria.
⁷ Department of Biomedical Engineering, University of Ilorin, Ilorin, Nigeria.

Abstract

Background: Schizophrenia affects about 1% of the global population. In addition to the complex etiology, linking this illness to genetic, environmental, and neurobiological factors, the dynamic experiences associated with this disease, such as experiences of delusions, hallucinations, disorganized thinking, and abnormal behaviors, limit neurological consensuses regarding mechanisms underlying this disease.

Methods: In this study, we recruited 72 patients with schizophrenia and 74 healthy individuals matched by age and sex to investigate the structural brain changes that may serve as prognostic biomarkers, indicating evidence of neural dysfunction underlying schizophrenia and subsequent cognitive and behavioral deficits. We used voxel-based morphometry (VBM) to determine these changes in the three tissue structures: the gray matter (GM), white matter (WM), and cerebrospinal fluid (CSF). For both image processing and statistical analysis, we used statistical parametric mapping (SPM).

Results: Our results show that patients with schizophrenia exhibited a significant volume reduction in both GM and WM. In particular, GM volume reductions were more evident in the frontal, temporal, limbic, and parietal lobe, similarly the WM volume reductions were predominantly in the frontal, temporal, and limbic lobe. In addition, patients with schizophrenia demonstrated a significant increase in the CSF volume in the left third and lateral ventricle regions.

Conclusion: This VBM study supports existing research showing that schizophrenia is associated with alterations in brain structure, including gray and white matter, and cerebrospinal fluid volume. These findings provide insights into the neurobiology of schizophrenia and may inform the development of more effective diagnostic and therapeutic approaches.

Keywords: cerebrospinal fluid; gray matter; schizophrenia; statistical parametric mapping; structural MRI; voxel-based morphometry; white matter.