Key Publications
The interpersonal computational psychiatry of social coordination in schizophrenia.
July 2023
Yafeng Pan , Yalan Wen , Jingwen Jin , Ji Chen
Impairments in social coordination form a core dimension of various psychiatric disorders, including schizophrenia. Advances in interpersonal and computational psychiatry support a major change in studying social coordination in schizophrenia. Although these developments provided novel perspectives to study how interpersonal activities shape coordination and to examine computational mechanisms, direct attempts to integrate the two methodologies have been sparse. Here, we propose an interpersonal computational framework that (1) leverages the active inference framework to model aberrant social coordination processes in schizophrenia and (2) incorporates dynamical system models to dissect intrapersonal and interpersonal synchronisation to inform a statistical model based on active inference. We discuss how this interpersonal computational psychiatry framework can elucidate the aberrant processes leading to psychopathology, with schizophrenia as an example, and highlight how it might aid clinical intervention and practice. Finally, we discuss challenges and opportunities for using the framework in studying social coordination impairments.
The interpersonal computational Leveraging machine learning for gaining neurobiological and nosological insights in psychiatric research.
January 2023
Ji Chen , Kaustubh R. Patil , B.T. Thomas Yeo , Simon B. Eickhoff
Much attention is currently devoted to developing diagnostic classifiers for mental disorders. Complementing these efforts, we highlight the potential of machine learning to gain biological insights into the psychopathology and nosology of mental disorders. Studies to this end have mainly used brain imaging data, which can be obtained noninvasively from large cohorts and have repeatedly been argued to reveal potentially intermediate phenotypes. This may become particularly relevant in light of recent efforts to identify magnetic resonance imaging–derived biomarkers that yield insight into pathophysiological processes as well as to refine the taxonomy of mental illness. In particular, the accuracy of machine learning models may be used as dependent variables to identify features relevant to pathophysiology. Moreover, such approaches may help disentangle the dimensional (within diagnosis) and often overlapping (across diagnoses) symptomatology of psychiatric illness. We also point out a multiview perspective that combines data from different sources, bridging molecular and system-level information. Finally, we summarize recent efforts toward a data-driven definition of subtypes or disease entities through unsupervised and semisupervised approaches. The latter, blending unsupervised and supervised concepts, may represent a particularly promising avenue toward dissecting heterogeneous categories. Finally, we raise several technical and conceptual aspects related to the reviewed approaches. In particular, we discuss common pitfalls pertaining to flawed input data or analytic procedures that would likely lead to unreliable outputs.
Mapping the deficit dimension structure of the National Institutes of Health Stroke Scale.
January 2023
Bastian Cheng , Ji Chen , Alina Königsberg , Carola Mayer , Leander Rimmele , Kaustubh R. Patil , Christian Gerloff , Götz Thomalla , Simon B. Eickhoff
The National Institutes of Health Stroke Scale (NIHSS) is the most frequently applied clinical rating scale for standardized assessment of neurological deficits in acute stroke in both clinical and research settings. Notwithstanding this prominent role, important questions regarding its validity remain insufficiently addressed: Investigations of the underlying dimensional structure of the NIHSS yielded inconsistent results that are largely not generalizable across studies. Neurobiological validations by linking measured deficit dimensions to brain anatomy and function are missing.
Promoting Computational Psychiatry in China.
March 2022
Haiyang Geng, Ji Chen, Hu Chuan-Peng, Jingwen Jin, Raymond C. K. Chan, Ying Li, Xiaoqing Hu, Ru-Yuan Zhang & Lei Zhang
Computational psychiatry holds promise for basic research and clinical practice in safeguarding mental health. In this Comment, we discuss why China needs computational psychiatry, why its development in China will benefit the field globally, and the challenges of promoting computational psychiatry in China and how to tackle them.
Intrinsic connectivity patterns of task-defined brain networks allow individual prediction of cognitive symptom dimension of schizophrenia and are linked to molecular architecture.
February 2021
Ji Chen , Veronika I. Müller , Juergen Dukart , Felix Hoffstaedter , Justin T. Baker , Avram J. Holmes , Deniz Vatansever , Thomas Nickl-Jockschat , Xiaojin Liu , Birgit Derntl , Lydia Kogler , Renaud Jardri , Oliver Gruber , André Aleman , Iris E. Sommer , Simon B. Eickhoff , Kaustubh R. Patil
Despite the marked interindividual variability in the clinical presentation of schizophrenia, the extent to which individual dimensions of psychopathology relate to the functional variability in brain networks among patients remains unclear. Here, we address this question using network-based predictive modeling of individual psychopathology along 4 data-driven symptom dimensions. Follow-up analyses assess the molecular underpinnings of predictive networks by relating them to neurotransmitter-receptor distribution patterns.
Neurobiological substrates of the positive formal thought disorder in schizophrenia revealed by seed connectome-based predictive modeling.
April 2021
Ji Chen , Tobias Wensing , Felix Hoffstaedter , Edna C. Cieslik , Veronika I. Müller , Kaustubh R. Patil , André Aleman , Birgit Derntl , Oliver Gruber , Renaud Jardri , Lydia Kogler , Iris E. Sommer , Simon B. Eickhoff , Thomas Nickl-Jockschat
Formal thought disorder (FTD) is a core symptom cluster of schizophrenia, but its neurobiological substrates remain poorly understood. Here we collected resting-state fMRI data from 276 subjects at seven sites and employed machine-learning to investigate the neurobiological correlates of FTD along positive and negative symptom dimensions in schizophrenia. Three a priori, meta-analytically defined FTD-related brain regions were used as seeds to generate whole-brain resting-state functional connectivity (rsFC) maps, which were then compared between schizophrenia patients and controls. A repeated cross-validation procedure was realized within the patient group to identify clusters whose rsFC patterns to the seeds were repeatedly observed as significantly associated with specific FTD dimensions. These repeatedly identified clusters (i.e., robust clusters) were functionally characterized and the rsFC patterns were used for predictive modeling to investigate predictive capacities for individual FTD dimensional-scores. Compared with controls, differential rsFC was found in patients in fronto-temporo-thalamic regions. Our cross-validation procedure revealed significant clusters only when assessing the seed-to-whole-brain rsFC patterns associated with positive-FTD. RsFC patterns of three fronto-temporal clusters, associated with higher-order cognitive processes (e.g., executive functions), specifically predicted individual positive-FTD scores (p = 0.005), but not other positive symptoms, and the PANSS general psychopathology subscale (p > 0.05). The prediction of positive-FTD was moreover generalized to an independent dataset (p = 0.013). Our study has identified neurobiological correlates of positive FTD in schizophrenia in a network associated with higher-order cognitive functions, suggesting a dysexecutive contribution to FTD in schizophrenia. We regard our findings as robust, as they allow a prediction of individual-level symptom severity.
Neurobiological divergence of the positive and negative schizophrenia subtypes identified on a new factor-structure of psychopathology using non-negative factorization: An international machine-learning study.
February 2020
Ji Chen , Kaustubh R. Patil , Susanne Weis , Kang Sim , Thomas Nickl-Jockschat , Juan Zhou , André Aleman , Iris E. Sommer , Edith J. Liemburg , Felix Hoffstaedter , Ute Habel , Birgit Derntl , Xiaojin Liu , Jona M. Fischer , Lydia Kogler , Christina Regenbogen , Vaibhav A. Diwadkar , Jeffrey A. Stanley , Valentin Riedl , Renaud Jardri …Ellen Visser
Disentangling psychopathological heterogeneity in schizophrenia is challenging, and previous results remain inconclusive. We employed advanced machine learning to identify a stable and generalizable factorization of the Positive and Negative Syndrome Scale and used it to identify psychopathological subtypes as well as their neurobiological differentiations.
Brain grey matter volume reduction and anxiety-like behavior in lipopolysaccharide-induced chronic pulmonary inflammation rats: A structural MRI study with histological validation.
February 2019
Ji Chen , Ya Yan , Fengjuan Yuan , Jianbo Cao , Shanhua Li , Simon B. Eickhoff , Jiaxing Zhang
While there have been multiple fMRI studies into the brain functional changes after acutely stimulated peripheral infection, knowledge for the effect of chronic peripheral infection on whole brain morphology is still quite limited. The present study was designed to investigate the brain structural and emotional changes after peripheral local infection initiated chronic systemic inflammation and the relationship between circulating inflammatory markers and brain grey matter. Specifically, in-vivo T2-weighted MRI was performed on rats with lipopolysaccharide (LPS)-induced chronic pulmonary inflammation (CPI) and those without. Grey matter volume was quantified using diffeomorphic anatomical registration through exponentiated lie (DARTEL) enhanced voxel-based morphometry followed by between-group comparison. Open field experiment was conducted to test the potential anxiety-like behaviors after CPI, along with the ELISA estimated inflammatory markers were correlated to grey matter volume. Guided by image findings, we undertook a focused histological investigation with immunefluorescence and Nissl staining. A widespread decrease of grey matter volume in CPI-model rats was revealed. 8 of the 12 measured inflammatory markers presented differential neuroanatomical correlation patterns with three of the pro-inflammatory cytokines (IL-1β, IL-6 and TNF-α) and CRP being the most notable. Lower grey matter volumes in some of the inflammatory markers related regions (amygdala, CA2 and cingulate cortex) were associated with more-severe anxiety-like behaviors. Furthermore, grey matter volumes in amygdala and CA3 were correlated negatively with the expressions of glial proteins (S100β and Nogo-A), while the grey matter volume in hypo-thalamus was changing positively with neural cell area. Overall, the neuroanatomical association patterns and the histopathology underpinning the MRI observations we demonstrated here would probably serve as one explanation for the cerebral and emotional deficits presented in the patients with CPI, which would furthermore yield new insights into the adverse effects the many other systemic inflammation and inflammatory autoimmune diseases would pose on brain morphology.
