Biqiu Tang 1, 2, Hui Sun 1, 2 , Naici Liu 1, 2, Youjin Zhao 1, 2, Chengming Yang 1, 2, Senhao Liu 1, 2, Qiyong Gong 1, 2, Wenjing Zhang 1, 2, *, Su Lui 1, 2, *
1Huaxi MR Research Center (HMRRC), Functional and Molecular Imaging Key Laboratory of Sichuan Province, Department of Radiology, West China Hospital of Sichuan University, Chengdu, China
2Research Unit of Psychoradiology, Chinese Academy of Medical Sciences, Chengdu, China
* To whom correspondence should be addressed:
Dr. Wenjing Zhang, No. 37 Guoxue Xiang, Chengdu 610041, China.
Email: wjzhang19@scu.edu.cn
Dr. Su Lui, No. 37 Guoxue Xiang, Chengdu 610041, China.
Email: lusuwcums@tom.com.
Introduction
With the increase of the pace of life, work pressure, work-family conflict, social changes and emergencies, the prevalence of mental disorders are increasing, which contributed to a large proportion of the global disease burden [1]. At present, the diagnosis and classifications of mental disorders in psychiatry has been relying on psychological and behavioral observations, while heterogeneity within psychiatric syndromes such as depression and psychosis in genetics, neurobiology and treatment outcomes was widely demonstrated in such way [2-5]. When diagnostic labels do not map precisely onto either biology or treatment outcome, it is challenging to conduct translational neuroscience research to extend the understanding of pathogenesis and develop treatments that will target alterations in specific patients for personalized treatment. In addition, since current diagnosis requires that the defining behavioral features are already present, it is difficult to develop targeted prevention-based interventions.
The progress in psychiatric neuroimaging, the notion for clinical translation and the birth of Psychoradiology
Since the first evidence of enlarged cerebral ventricles in patients with schizophrenia revealed in Computer Tomography (CT) in 1976 [6], non-invasive neuroimaging has brought great enthusiasm and hope for identifying the specific neuropathology of patients with mental disorders in vivo. Indeed, a variety of neuroimaging researches have contributed to explore potential neural substrates of mental disorders, with the structural, functional, and metabolic brain representations of patients being comprehensively characterized [7]. Though heterogeneity is widely demonstrated in previous findings, some consistent findings have been identified that might be closely related to clinical features and etiologic factors [8], which is important for mechanic understanding of the pathogenesis and neuropathology. After decades of exploratory clinical neuroimaging research, laboratories are working to translate that experience into clinically actionable biological assessments to develop biomarkers that can complement clinical psychiatric evaluations for optimizing and individualizing diagnosis, prognosis, and treatment planning.
Taking individual differences in human brain into consideration is a critical prerequisite for personalized diagnosis and treatment, which is normalized in most neuroimaging studies that analyzed the data at the group level. Machine learning approaches, however, could capitalize on multivariate data to detect complex patterns of brain abnormalities in mental disorders, thus can potentially use the identified biological or behavioral features in combination to predict illness onset in high-risk individuals, differentiate patients with schizophrenia from healthy individuals, and predict the long-term prognostic outcomes before the administration of routine medications at the individual level [9-11]. These findings represent an important step towards patient identification and stratification on the basis of neurobiological abnormalities to complement behavioral analysis currently guiding psychiatric diagnostic practice and prognostic prediction.
With the notion to provide objective and reliable imaging biomarkers that are helpful in patient stratification and prognosis prediction for those with mental disorders, a new subspecialty in the field of neuroradiology emerged known as Psychoradiology [7, 9, 20-23]. At present, identifying neuroimaging-defined patient subgroups biologically is under active investigation from the Psychoradiologic perspective, which have met with greater success, particularly in the areas of mood and psychotic disorders. The promise of this area is that by resolving illness heterogeneity in biological neuroimaging terms, drug development and therapeutic interventions could shift towards altering specific targeted biological processes instead of working to change complex behavioral features [10, 11, 24, 25]. More importantly, quantitative MR Psychoradiology Examinations have been initially issued to aid clinical diagnosis for high-risk individuals and TMS treatment planning for those who had been treatment-resistant. In future work, large-scale multicenter study that collect near-to-identical data across sites is necessary to examine the generalizability and speed the clinical translation of the abovementioned findings.
Conclusion
To identify, validate and establish the clinical utility of neuroimaging-defined profiles of patients with psychiatric syndromes, imaging markers need to be considered with an applied purpose in mind, procedures for data collection and processing need to be established, and optimal quantification methods of preferred biomarkers need to be established. In this perspective, Psychoradiology might hold great promise for these purposes to facilitate personalized medicine of patients affected by mental disorders.
References
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