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Chen Z, Hu B, Liu X, Becker B, Eickhoff SB, Miao K, Gu X, Tang Y, Dai X, Li C, Leonov A, Xiao Z, Feng Z, Chen J, Chuan-Peng H. Sampling inequalities affect generalization of neuroimaging-based diagnostic classifiers in psychiatry. BMC Med 2023; 21:241. [PMID: 37400814 DOI: 10.1186/s12916-023-02941-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/10/2023] [Accepted: 06/13/2023] [Indexed: 07/05/2023] Open
Abstract
BACKGROUND The development of machine learning models for aiding in the diagnosis of mental disorder is recognized as a significant breakthrough in the field of psychiatry. However, clinical practice of such models remains a challenge, with poor generalizability being a major limitation. METHODS Here, we conducted a pre-registered meta-research assessment on neuroimaging-based models in the psychiatric literature, quantitatively examining global and regional sampling issues over recent decades, from a view that has been relatively underexplored. A total of 476 studies (n = 118,137) were included in the current assessment. Based on these findings, we built a comprehensive 5-star rating system to quantitatively evaluate the quality of existing machine learning models for psychiatric diagnoses. RESULTS A global sampling inequality in these models was revealed quantitatively (sampling Gini coefficient (G) = 0.81, p < .01), varying across different countries (regions) (e.g., China, G = 0.47; the USA, G = 0.58; Germany, G = 0.78; the UK, G = 0.87). Furthermore, the severity of this sampling inequality was significantly predicted by national economic levels (β = - 2.75, p < .001, R2adj = 0.40; r = - .84, 95% CI: - .41 to - .97), and was plausibly predictable for model performance, with higher sampling inequality for reporting higher classification accuracy. Further analyses showed that lack of independent testing (84.24% of models, 95% CI: 81.0-87.5%), improper cross-validation (51.68% of models, 95% CI: 47.2-56.2%), and poor technical transparency (87.8% of models, 95% CI: 84.9-90.8%)/availability (80.88% of models, 95% CI: 77.3-84.4%) are prevailing in current diagnostic classifiers despite improvements over time. Relating to these observations, model performances were found decreased in studies with independent cross-country sampling validations (all p < .001, BF10 > 15). In light of this, we proposed a purpose-built quantitative assessment checklist, which demonstrated that the overall ratings of these models increased by publication year but were negatively associated with model performance. CONCLUSIONS Together, improving sampling economic equality and hence the quality of machine learning models may be a crucial facet to plausibly translating neuroimaging-based diagnostic classifiers into clinical practice.
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Affiliation(s)
- Zhiyi Chen
- Experimental Research Center for Medical and Psychological Science (ERC-MPS), School of Psychology, Third Military Medical University, Chongqing, China.
- Faculty of Psychology, Southwest University, Chongqing, China.
| | - Bowen Hu
- Faculty of Psychology, Southwest University, Chongqing, China
| | - Xuerong Liu
- Experimental Research Center for Medical and Psychological Science (ERC-MPS), School of Psychology, Third Military Medical University, Chongqing, China
| | - Benjamin Becker
- The Center of Psychosomatic Medicine, Sichuan Provincial Center for Mental Health, Sichuan Provincial People's Hospital, Chengdu, China
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Laboratory for Neuroinformation, University of Electronic Science and Technology of China, Chengdu, China
| | - Simon B Eickhoff
- Institute of Systems Neuroscience, Heinrich Heine University Düsseldorf, Düsseldorf, Germany
| | - Kuan Miao
- Experimental Research Center for Medical and Psychological Science (ERC-MPS), School of Psychology, Third Military Medical University, Chongqing, China
| | - Xingmei Gu
- Experimental Research Center for Medical and Psychological Science (ERC-MPS), School of Psychology, Third Military Medical University, Chongqing, China
| | - Yancheng Tang
- School of Business and Management, Shanghai International Studies University, Shanghai, China
| | - Xin Dai
- Faculty of Psychology, Southwest University, Chongqing, China
| | - Chao Li
- Department of Radiology, The Third Affiliated Hospital, Sun Yat-Sen University, Guangdong, China
| | - Artemiy Leonov
- School of Psychology, Clark University, Worcester, MA, USA
| | - Zhibing Xiao
- State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing, China
| | - Zhengzhi Feng
- Experimental Research Center for Medical and Psychological Science (ERC-MPS), School of Psychology, Third Military Medical University, Chongqing, China
| | - Ji Chen
- Department of Psychology and Behavioral Sciences, Zhejiang University, Hangzhou, China.
- Department of Psychiatry, The Fourth Affiliated Hospital, Zhejiang University School of Medicine, Yiwu, Zhejiang, China.
| | - Hu Chuan-Peng
- School of Psychology, Nanjing Normal University, Nanjing, China
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Chen Z, Liu X, Yang Q, Wang YJ, Miao K, Gong Z, Yu Y, Leonov A, Liu C, Feng Z, Chuan-Peng H. Evaluation of Risk of Bias in Neuroimaging-Based Artificial Intelligence Models for Psychiatric Diagnosis: A Systematic Review. JAMA Netw Open 2023; 6:e231671. [PMID: 36877519 PMCID: PMC9989906 DOI: 10.1001/jamanetworkopen.2023.1671] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 03/07/2023] Open
Abstract
IMPORTANCE Neuroimaging-based artificial intelligence (AI) diagnostic models have proliferated in psychiatry. However, their clinical applicability and reporting quality (ie, feasibility) for clinical practice have not been systematically evaluated. OBJECTIVE To systematically assess the risk of bias (ROB) and reporting quality of neuroimaging-based AI models for psychiatric diagnosis. EVIDENCE REVIEW PubMed was searched for peer-reviewed, full-length articles published between January 1, 1990, and March 16, 2022. Studies aimed at developing or validating neuroimaging-based AI models for clinical diagnosis of psychiatric disorders were included. Reference lists were further searched for suitable original studies. Data extraction followed the CHARMS (Checklist for Critical Appraisal and Data Extraction for Systematic Reviews of Prediction Modeling Studies) and PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-analyses) guidelines. A closed-loop cross-sequential design was used for quality control. The PROBAST (Prediction Model Risk of Bias Assessment Tool) and modified CLEAR (Checklist for Evaluation of Image-Based Artificial Intelligence Reports) benchmarks were used to systematically evaluate ROB and reporting quality. FINDINGS A total of 517 studies presenting 555 AI models were included and evaluated. Of these models, 461 (83.1%; 95% CI, 80.0%-86.2%) were rated as having a high overall ROB based on the PROBAST. The ROB was particular high in the analysis domain, including inadequate sample size (398 of 555 models [71.7%; 95% CI, 68.0%-75.6%]), poor model performance examination (with 100% of models lacking calibration examination), and lack of handling data complexity (550 of 555 models [99.1%; 95% CI, 98.3%-99.9%]). None of the AI models was perceived to be applicable to clinical practices. Overall reporting completeness (ie, number of reported items/number of total items) for the AI models was 61.2% (95% CI, 60.6%-61.8%), and the completeness was poorest for the technical assessment domain with 39.9% (95% CI, 38.8%-41.1%). CONCLUSIONS AND RELEVANCE This systematic review found that the clinical applicability and feasibility of neuroimaging-based AI models for psychiatric diagnosis were challenged by a high ROB and poor reporting quality. Particularly in the analysis domain, ROB in AI diagnostic models should be addressed before clinical application.
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Affiliation(s)
- Zhiyi Chen
- School of Psychology, Third Military Medical University, Chongqing, China
- Experimental Research Center for Medical and Psychological Science, Third Military Medical University, Chongqing, China
| | - Xuerong Liu
- School of Psychology, Third Military Medical University, Chongqing, China
- Experimental Research Center for Medical and Psychological Science, Third Military Medical University, Chongqing, China
| | - Qingwu Yang
- Department of Neurology, Daping Hospital, Third Military Medical University, Chongqing, China
| | - Yan-Jiang Wang
- Department of Neurology, Daping Hospital, Third Military Medical University, Chongqing, China
| | - Kuan Miao
- School of Psychology, Third Military Medical University, Chongqing, China
- Experimental Research Center for Medical and Psychological Science, Third Military Medical University, Chongqing, China
| | - Zheng Gong
- School of Psychology, Third Military Medical University, Chongqing, China
- Experimental Research Center for Medical and Psychological Science, Third Military Medical University, Chongqing, China
| | - Yang Yu
- School of Psychology, Third Military Medical University, Chongqing, China
| | - Artemiy Leonov
- Department of Psychology, Clark University, Worcester, Massachusetts
| | - Chunlei Liu
- School of Psychology, Qufu Normal University, Qufu, China
| | - Zhengzhi Feng
- School of Psychology, Third Military Medical University, Chongqing, China
- Experimental Research Center for Medical and Psychological Science, Third Military Medical University, Chongqing, China
| | - Hu Chuan-Peng
- School of Psychology, Nanjing Normal University, Nanjing, China
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Antonucci LA, Fazio L, Pergola G, Blasi G, Stolfa G, Di Palo P, Mucci A, Rocca P, Brasso C, di Giannantonio M, Maria Giordano G, Monteleone P, Pompili M, Siracusano A, Bertolino A, Galderisi S, Maj M. Joint structural-functional magnetic resonance imaging features are associated with diagnosis and real-world functioning in patients with schizophrenia. Schizophr Res 2022; 240:193-203. [PMID: 35032904 DOI: 10.1016/j.schres.2021.12.029] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/08/2021] [Revised: 10/20/2021] [Accepted: 12/22/2021] [Indexed: 12/28/2022]
Abstract
OBJECTIVE Earlier evidence suggested that structural-functional covariation in schizophrenia patients (SCZ) is associated with cognition, a predictor of functioning. Moreover, studies suggested that functional brain abnormalities of schizophrenia may be related with structural network features. However, only few studies have investigated the relationship between structural-functional covariation and both diagnosis and functioning in SCZ. We hypothesized that structural-functional covariation networks associated with diagnosis are related to real-world functioning in SCZ. METHODS We performed joint Independent Component Analysis on T1 images and resting-state fMRI-based Degree Centrality (DC) maps from 89 SCZ and 285 controls. Structural-functional covariation networks in which we found a main effect of diagnosis underwent correlation analysis to investigate their relationship with functioning. Covariation networks showing a significant association with both diagnosis and functioning underwent univariate analysis to better characterize group-level differences at the spatial level. RESULTS A structural-functional covariation network characterized by frontal, temporal, parietal and thalamic structural estimates significantly covaried with temporo-parietal resting-state DC. Compared with controls, SCZ had reduced structural-functional covariation within this network (pFDR = 0.005). The same measure correlated positively with both social and occupational functioning (both pFDR = 0.042). Univariate analyses revealed grey matter deviations in SCZ compared with controls within this structural-functional network in hippocampus, cerebellum, thalamus, orbito-frontal cortex, and insula. No group differences were found in DC. CONCLUSIONS Findings support the existence of a phenotypical association between group-level differences and inter-individual heterogeneity of functional deficits in SCZ. Given that only the joint structural/functional analysis revealed this association, structural-functional covariation may be a potentially relevant schizophrenia phenotype.
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Affiliation(s)
- Linda A Antonucci
- Department of Basic Medical Sciences, Neuroscience and Sense Organs, University of Bari Aldo Moro, Bari, Italy
| | - Leonardo Fazio
- Department of Basic Medical Sciences, Neuroscience and Sense Organs, University of Bari Aldo Moro, Bari, Italy
| | - Giulio Pergola
- Department of Basic Medical Sciences, Neuroscience and Sense Organs, University of Bari Aldo Moro, Bari, Italy
| | - Giuseppe Blasi
- Department of Basic Medical Sciences, Neuroscience and Sense Organs, University of Bari Aldo Moro, Bari, Italy
| | - Giuseppe Stolfa
- Department of Basic Medical Sciences, Neuroscience and Sense Organs, University of Bari Aldo Moro, Bari, Italy
| | - Piergiuseppe Di Palo
- Department of Basic Medical Sciences, Neuroscience and Sense Organs, University of Bari Aldo Moro, Bari, Italy
| | - Armida Mucci
- Department of Psychiatry, University of Campania "Luigi Vanvitelli", Naples, Italy
| | - Paola Rocca
- Department of Neuroscience, Section of Psychiatry, University of Turin, Turin, Italy
| | - Claudio Brasso
- Department of Neuroscience, Section of Psychiatry, University of Turin, Turin, Italy
| | | | | | - Palmiero Monteleone
- Department of Medicine, Surgery and Dentistry "Scuola Medica Salernitana", Section of Neuroscience, University of Salerno, Salerno, Italy
| | - Maurizio Pompili
- Department of Neurosciences, Mental Health, and Sensory Organs, S. Andrea Hospital, Sapienza University of Rome, Rome, Italy
| | - Alberto Siracusano
- Department of Systems Medicine, Psychiatry and Clinical Psychology Unit, Tor Vergata University of Rome, Rome, Italy
| | - Alessandro Bertolino
- Department of Basic Medical Sciences, Neuroscience and Sense Organs, University of Bari Aldo Moro, Bari, Italy.
| | - Silvana Galderisi
- Department of Psychiatry, University of Campania "Luigi Vanvitelli", Naples, Italy
| | - Mario Maj
- Department of Psychiatry, University of Campania "Luigi Vanvitelli", Naples, Italy
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Thalamic connectivity system across psychiatric disorders: Current status and clinical implications. BIOLOGICAL PSYCHIATRY GLOBAL OPEN SCIENCE 2021; 2:332-340. [PMID: 36324665 PMCID: PMC9616255 DOI: 10.1016/j.bpsgos.2021.09.008] [Citation(s) in RCA: 18] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/28/2021] [Revised: 09/23/2021] [Accepted: 09/25/2021] [Indexed: 12/20/2022] Open
Abstract
The thalamic connectivity system, with the thalamus as the central node, enables transmission of the brain’s neural computations via extensive connections to cortical, subcortical, and cerebellar regions. Emerging reports suggest deficits in this system across multiple psychiatric disorders, making it a unique network of high translational and transdiagnostic utility in mapping neural alterations that potentially contribute to symptoms and disturbances in psychiatric patients. However, despite considerable research effort, it is still debated how this system contributes to psychiatric disorders. This review characterizes current knowledge regarding thalamic connectivity system deficits in psychiatric disorders, including schizophrenia, bipolar disorder, major depressive disorder, and autism spectrum disorder, across multiple levels of the system. We identify the presence of common and distinct patterns of deficits in the thalamic connectivity system in major psychiatric disorders and assess their nature and characteristics. Specifically, this review assembles evidence for the hypotheses of 1) thalamic microstructure, particularly in the mediodorsal nucleus, as a state marker of psychosis; 2) thalamo-prefrontal connectivity as a trait marker of psychosis; and 3) thalamo-somatosensory/parietal connectivity as a possible marker of general psychiatric illness. Furthermore, possible mechanisms contributing to thalamocortical dysconnectivity are explored. We discuss current views on the contributions of cerebellar-thalamic connectivity to the thalamic connectivity system and propose future studies to examine its effects at multiple levels, from the molecular (e.g., glutamatergic) to the behavioral (e.g., cognition), to gain a deeper understanding of the mechanisms that underlie the disturbances observed in psychiatric disorders.
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Antonucci LA, Penzel N, Pigoni A, Dominke C, Kambeitz J, Pergola G. Flexible and specific contributions of thalamic subdivisions to human cognition. Neurosci Biobehav Rev 2021; 124:35-53. [PMID: 33497787 DOI: 10.1016/j.neubiorev.2021.01.014] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/27/2020] [Revised: 08/30/2020] [Accepted: 01/04/2021] [Indexed: 11/17/2022]
Abstract
The thalamus participates in multiple functional brain networks supporting different cognitive abilities. How thalamo-cortical connections map onto the architecture of human cognition remains an outstanding question. The aim of this meta-analysis is to map co-activation between thalamic and extra-thalamic brain regions onto separate cognitive domains and to assess thalamic subdivision specificity within each of the cognitive domains considered. We parsed 93 fMRI studies into twelve cognitive domains. Signed Differential Mapping served to obtain co-activation maps. We then projected the contribution of thalamic subdivisions onto a thalamic atlas to assess cognitive domain specificity. A set of brain regions was flexibly involved with thalamus in several cognitive domains. Thalamic subdivisions showed ample cognitive heterogeneity. Our proposed model represents thalamic involvement in cognition as an "ensemble" of functional subdivisions with common cell properties embedded in separate cortical circuits rather than a homogeneous functional unit.
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Affiliation(s)
- Linda A Antonucci
- Department of Education, Psychology and Communication - University of Bari Aldo Moro, Bari, Italy; Section for Neurodiagnostic Applications, Department of Psychiatry and Psychotherapy - Ludwig Maximilians Universität, Munich, Germany; Department of Basic Medical Sciences, Neuroscience and Sense Organs - University of Bari Aldo Moro, Bari, Italy.
| | - Nora Penzel
- Section for Neurodiagnostic Applications, Department of Psychiatry and Psychotherapy - Ludwig Maximilians Universität, Munich, Germany; Department of Psychiatry University of Cologne, Medical Faculty Cologne Germany
| | - Alessandro Pigoni
- Section for Neurodiagnostic Applications, Department of Psychiatry and Psychotherapy - Ludwig Maximilians Universität, Munich, Germany; Department of Neurosciences and Mental Health - Fondazione IRCCS Ca' Granda Ospedale Maggiore Policlinico, University of Milan, Milan, Italy
| | - Clara Dominke
- Section for Neurodiagnostic Applications, Department of Psychiatry and Psychotherapy - Ludwig Maximilians Universität, Munich, Germany
| | - Joseph Kambeitz
- Department of Psychiatry University of Cologne, Medical Faculty Cologne Germany
| | - Giulio Pergola
- Department of Basic Medical Sciences, Neuroscience and Sense Organs - University of Bari Aldo Moro, Bari, Italy; Lieber Institute for Brain Development, Johns Hopkins Medical Campus, Baltimore, MD, USA.
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Hur JW, Kim T, Cho KIK, Kwon JS. Attenuated Resting-State Functional Anticorrelation between Attention and Executive Control Networks in Schizotypal Personality Disorder. J Clin Med 2021; 10:jcm10020312. [PMID: 33467694 PMCID: PMC7829946 DOI: 10.3390/jcm10020312] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/15/2020] [Revised: 01/08/2021] [Accepted: 01/12/2021] [Indexed: 11/18/2022] Open
Abstract
Exploring the disruptions to intrinsic resting-state networks (RSNs) in schizophrenia-spectrum disorders yields a better understanding of the disease-specific pathophysiology. However, our knowledge of the neurobiological underpinnings of schizotypal personality disorders mostly relies on research on schizotypy or schizophrenia. This study aimed to investigate the RSN abnormalities of schizotypal personality disorder (SPD) and their clinical implications. Using resting-state data, the intra- and inter-network of the higher-order functional networks (default mode network, DMN; frontoparietal network, FPN; dorsal attention network, DAN; salience network, SN) were explored in 22 medication-free, community-dwelling, non-help seeking individuals diagnosed with SPD and 30 control individuals. Consequently, while there were no group differences in intra-network functional connectivity across DMN, FPN, DAN, and SN, the SPD participants exhibited attenuated anticorrelation between the right frontal eye field region of the DAN and the right posterior parietal cortex region of the FPN. The decreases in anticorrelation were correlated with increased cognitive–perceptual deficits and disorganization factors of the schizotypal personality questionnaire, as well as reduced independence–performance of the social functioning scale for all participants together. This study, which links SPD pathology and social functioning deficits, is the first evidence of impaired large-scale intrinsic brain networks in SPD.
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Affiliation(s)
- Ji-Won Hur
- Department of Psychology, Korea University, Seoul 02841, Korea;
- Department of Brain and Cognitive Sciences, Seoul National University College of Natural Sciences, Seoul 08826, Korea;
| | - Taekwan Kim
- Department of Brain and Cognitive Sciences, Seoul National University College of Natural Sciences, Seoul 08826, Korea;
| | - Kang Ik K. Cho
- Department of Psychiatry, Brigham and Women’s Hospital, Harvard Medical School, Boston, MA 02215, USA;
| | - Jun Soo Kwon
- Department of Brain and Cognitive Sciences, Seoul National University College of Natural Sciences, Seoul 08826, Korea;
- Department of Psychiatry, Seoul National University College of Medicine, Seoul 03080, Korea
- Institute of Human Behavioral Medicine, SNU-MRC, Seoul 03080, Korea
- Correspondence: ; Tel.: +82-2-2072-2972; Fax: +82-2-747-9063
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Liu Y, Huang L, Tong Y, Chen J, Gao D, Yang F. Association of retinal nerve fiber abnormalities with serum CNTF and cognitive functions in schizophrenia patients. PeerJ 2020; 8:e9279. [PMID: 32676219 PMCID: PMC7335503 DOI: 10.7717/peerj.9279] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/08/2019] [Accepted: 05/12/2020] [Indexed: 12/13/2022] Open
Abstract
BACKGROUND Recent studies have reported reductions in retinal nerve fiber layers (RNFL) in schizophrenia. Ciliary neurotrophic factor (CNTF) has shown protective effects on both the neurogenesis and retina. This study aimed at investigating retinal abnormalities and establishing their correlation with serum CNTF and cognitive impairments in schizophrenic Chinese patients. METHODS In total, 221 patients diagnosed with schizophrenia and 149 healthy controls were enrolled. Serum CNTF and clinical features of patients were investigated. Cognitive functions were evaluated with Repeatable Battery for the Assessment of Neuropsychology Status (RBANS). RNFL thickness and macular thickness (MT) of both eyes were measured with optical coherence tomography (OCT). T-tests and analysis of covariance were used to compare the variables between the patient and control groups, while multiple linear regression analysis was performed to determine the associations of RNFL thickness, CNTF and cognitive impairments. RESULTS RNFL was found thinner in patients than in healthy controls (right: 88.18 ± 25.84 µm vs.102.13 ± 14.32 µm, p = 0.001; left: 92.84 ± 13.54 µm vs.103.71 ± 11.94 µm, p < 0.001). CNTF was lower in the schizophrenia group (1755.45 ± 375.73 pg/ml vs. 1909.99 ± 368.08 pg/ml, p = 0.001). Decline in RNFL thickness was found correlated with course of illness and serum CNTF in patients (all p < 0.05). Similarly, cognitive functions such as immediate memory and visuospatial functions were also found correlated with decline in RNFL thickness. CONCLUSION Decline in RNFL thickness was associated with cognitive impairments of schizophrenia and CNFT serum concentration. The possibility of reduction in RNFL thickness as a biomarker for schizophrenia needs to be further examined.
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Affiliation(s)
- Yanhong Liu
- Peking University Huilongguan Clinical Medical School, Beijing Huilongguan Hospital, Peking University, Beijing, China
| | - Lvzhen Huang
- People's Hospital of Peking University, Peking University, Beijing, China
| | - Yongsheng Tong
- Peking University Huilongguan Clinical Medical School, Beijing Huilongguan Hospital, Peking University, Beijing, China
| | - Jingxu Chen
- Peking University Huilongguan Clinical Medical School, Beijing Huilongguan Hospital, Peking University, Beijing, China
| | - Dongfang Gao
- Peking University Huilongguan Clinical Medical School, Beijing Huilongguan Hospital, Peking University, Beijing, China
| | - Fude Yang
- Peking University Huilongguan Clinical Medical School, Beijing Huilongguan Hospital, Peking University, Beijing, China
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