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Di Camillo F, Grimaldi DA, Cattarinussi G, Di Giorgio A, Locatelli C, Khuntia A, Enrico P, Brambilla P, Koutsouleris N, Sambataro F. Magnetic resonance imaging-based machine learning classification of schizophrenia spectrum disorders: a meta-analysis. Psychiatry Clin Neurosci 2024. [PMID: 39290174 DOI: 10.1111/pcn.13736] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/29/2024] [Revised: 07/31/2024] [Accepted: 08/19/2024] [Indexed: 09/19/2024]
Abstract
BACKGROUND Recent advances in multivariate pattern recognition have fostered the search for reliable neuroimaging-based biomarkers in psychiatric conditions, including schizophrenia. These approaches consider the complex pattern of alterations in brain function and structure, overcoming the limitations of traditional univariate methods. To assess the reliability of neuroimaging-based biomarkers and the contribution of study characteristics in distinguishing individuals with schizophrenia spectrum disorder (SSD) from healthy controls (HCs), we conducted a systematic review of the studies that used multivariate pattern recognition for this objective. METHODS We systematically searched PubMed, Scopus, and Web of Science for studies on SSD classification using multivariate pattern analysis on magnetic resonance imaging data. We employed a bivariate random-effects meta-analytic model to explore the classification of sensitivity (SE) and specificity (SP) across studies while also evaluating the moderator effects of clinical and non-clinical variables. RESULTS A total of 119 studies (with 12,723 patients with SSD and 13,196 HCs) were identified. The meta-analysis estimated a SE of 79.1% (95% confidence interval [CI], 77.1%-81.0%) and a SP of 80.0% (95% CI, 77.8%-82.0%). In particular, the Positive and Negative Syndrome Scale and the Global Assessment of Functioning scores, age, age of onset, duration of untreated psychosis, deep learning, algorithm type, features selection, and validation methods had significant effects on classification performance. CONCLUSIONS Multivariate pattern analysis reliably identifies neuroimaging-based biomarkers of SSD, achieving ∼80% SE and SP. Despite clinical heterogeneity, discernible brain modifications effectively differentiate SSD from HCs. Classification performance depends on patient-related and methodological factors crucial for the development, validation, and application of prospective models in clinical settings.
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Affiliation(s)
- Fabio Di Camillo
- Department of Neuroscience (DNS), University of Padova, Padua, Italy
| | | | - Giulia Cattarinussi
- Department of Neuroscience (DNS), University of Padova, Padua, Italy
- Padova Neuroscience Center, University of Padova, Padua, Italy
- Department of Psychological Medicine, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, United Kingdom
| | - Annabella Di Giorgio
- Department of Mental Health and Addictions, ASST Papa Giovanni XXIII, Bergamo, Italy
| | - Clara Locatelli
- Department of Mental Health and Addictions, ASST Papa Giovanni XXIII, Bergamo, Italy
| | - Adyasha Khuntia
- Department of Psychiatry and Psychotherapy, Ludwig-Maximilian University, Munich, Germany
- International Max Planck Research School for Translational Psychiatry (IMPRS-TP), Munich, Germany
- Max-Planck-Institute of Psychiatry, Munich, Germany
| | - Paolo Enrico
- Department of Psychiatry and Psychotherapy, Ludwig-Maximilian University, Munich, Germany
- Department of Pathophysiology and Transplantation, University of Milan, Milan, Italy
- Department of Neurosciences and Mental Health, Fondazione IRCSS Ca' Granda Ospedale Maggiore Policlinico, Milan, Italy
| | - Paolo Brambilla
- Department of Pathophysiology and Transplantation, University of Milan, Milan, Italy
- Department of Neurosciences and Mental Health, Fondazione IRCSS Ca' Granda Ospedale Maggiore Policlinico, Milan, Italy
| | - Nikolaos Koutsouleris
- Max-Planck-Institute of Psychiatry, Munich, Germany
- Department of Psychiatry, Munich University Hospital, Munich, Germany
- Department of Psychosis Studies, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, United Kingdom
| | - Fabio Sambataro
- Department of Neuroscience (DNS), University of Padova, Padua, Italy
- Padova Neuroscience Center, University of Padova, Padua, Italy
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Yin Y, Lyu X, Zhou J, Yu K, Huang M, Shen G, Hao C, Wang Z, Yu H, Gao B. Cerebral cortex functional reorganization in preschool children with congenital sensorineural hearing loss: a resting-state fMRI study. Front Neurol 2024; 15:1423956. [PMID: 38988601 PMCID: PMC11234816 DOI: 10.3389/fneur.2024.1423956] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/26/2024] [Accepted: 06/07/2024] [Indexed: 07/12/2024] Open
Abstract
Purpose How cortical functional reorganization occurs after hearing loss in preschool children with congenital sensorineural hearing loss (CSNHL) is poorly understood. Therefore, we used resting-state functional MRI (rs-fMRI) to explore the characteristics of cortical reorganization in these patents. Methods Sixty-three preschool children with CSNHL and 32 healthy controls (HCs) were recruited, and the Categories of Auditory Performance (CAP) scores were determined at the 6-month follow-up after cochlear implantation (CI). First, rs-fMRI data were preprocessed, and amplitude of low-frequency fluctuation (ALFF) and regional homogeneity (ReHo) were calculated. Second, whole-brain functional connectivity (FC) analysis was performed using bilateral primary auditory cortex as seed points. Finally, Spearman correlation analysis was performed between the differential ALFF, ReHo and FC values and the CAP score. Results ALFF analysis showed that preschool children with CSNHL had lower ALFF values in the bilateral prefrontal cortex and superior temporal gyrus than HCs, but higher ALFF values in the bilateral thalamus and calcarine gyrus. And correlation analysis showed that some abnormal brain regions were weak negatively correlated with CAP score (p < 0.05). The ReHo values in the bilateral superior temporal gyrus, part of the prefrontal cortex and left insular gyrus were lower, whereas ReHo values in the bilateral thalamus, right caudate nucleus and right precentral gyrus were higher, in children with CSNHL than HCs. However, there was no correlation between ReHo values and the CAP scores (p < 0.05). Using primary auditory cortex (PAC) as seed-based FC further analysis revealed enhanced FC in the visual cortex, proprioceptive cortex and motor cortex. And there were weak negative correlations between the FC values in the bilateral superior temporal gyrus, occipital lobe, left postcentral gyrus and right thalamus were weakly negatively correlated and the CAP score (p < 0.05). Conclusion After auditory deprivation in preschool children with CSNHL, the local functions of auditory cortex, visual cortex, prefrontal cortex and somatic motor cortex are changed, and the prefrontal cortex plays a regulatory role in this process. There is functional reorganization or compensation between children's hearing and these areas, which may not be conducive to auditory language recovery after CI in deaf children.
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Affiliation(s)
- Yi Yin
- Department of Radiology, Affiliated Hospital of Guizhou Medical University, Guiyang, China
| | - Xinyue Lyu
- Guizhou Medical University, Guiyang, China
| | - Jian Zhou
- Department of Radiology, Affiliated Hospital of Guizhou Medical University, Guiyang, China
| | - Kunlin Yu
- The Key Laboratory for Chemistry of Natural Product of Guizhou Province, Guizhou Medical University, Guiyang, China
| | - Mingming Huang
- Department of Radiology, Affiliated Hospital of Guizhou Medical University, Guiyang, China
| | - Guiquan Shen
- Department of Radiology, Affiliated Hospital of Guizhou Medical University, Guiyang, China
| | - Cheng Hao
- Department of Radiology, Affiliated Hospital of Guizhou Medical University, Guiyang, China
| | - Zhengfu Wang
- Department of Radiology, Affiliated Hospital of Guizhou Medical University, Guiyang, China
| | - Hui Yu
- Department of Radiology, Dermatology Hospital of Southern Medical University, Guangzhou, China
| | - Bo Gao
- Department of Radiology, Affiliated Hospital of Guizhou Medical University, Guiyang, China
- Key Laboratory of Brain Imaging, Guizhou Medical University, Guiyang, China
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Merola GP, Tarchi L, Saccaro LF, Delavari F, Piguet C, Van De Ville D, Castellini G, Ricca V. Transdiagnostic markers across the psychosis continuum: a systematic review and meta-analysis of resting state fMRI studies. Front Psychiatry 2024; 15:1378439. [PMID: 38895037 PMCID: PMC11184053 DOI: 10.3389/fpsyt.2024.1378439] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/29/2024] [Accepted: 04/26/2024] [Indexed: 06/21/2024] Open
Abstract
Psychotic symptoms are among the most debilitating and challenging presentations of severe psychiatric diseases, such as schizophrenia, schizoaffective, and bipolar disorder. A pathophysiological understanding of intrinsic brain activity underlying psychosis is crucial to improve diagnosis and treatment. While a potential continuum along the psychotic spectrum has been recently described in neuroimaging studies, especially for what concerns absolute and relative amplitude of low-frequency fluctuations (ALFF and fALFF), these efforts have given heterogeneous results. A transdiagnostic meta-analysis of ALFF/fALFF in patients with psychosis compared to healthy controls is currently lacking. Therefore, in this pre-registered systematic review and meta-analysis PubMed, Scopus, and Embase were searched for articles comparing ALFF/fALFF between psychotic patients and healthy controls. A quantitative synthesis of differences in (f)ALFF between patients along the psychotic spectrum and healthy controls was performed with Seed-based d Mapping, adjusting for age, sex, duration of illness, clinical severity. All results were corrected for multiple comparisons by Family-Wise Error rates. While lower ALFF and fALFF were detected in patients with psychosis in comparison to controls, no specific finding survived correction for multiple comparisons. Lack of this correction might explain the discordant findings highlighted in previous literature. Other potential explanations include methodological issues, such as the lack of standardization in pre-processing or analytical procedures among studies. Future research on ALFF/fALFF differences for patients with psychosis should prioritize the replicability of individual studies. Systematic review registration https://osf.io/, identifier (ycqpz).
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Affiliation(s)
| | - Livio Tarchi
- Psychiatry Unit, Department of Health Sciences, University of Florence, Florence, Italy
| | - Luigi F. Saccaro
- Psychiatry Department, Geneva University Hospital and Faculty of Medicine, Geneva University Hospital, Geneva, Switzerland
| | - Farnaz Delavari
- Neuro-X Institute, École Polytechnique Fédérale de Lausanne, Geneva, Switzerland
- Developmental Imaging and Psychopathology Laboratory, University of Geneva School of Medicine, Geneva, Switzerland
| | - Camille Piguet
- Psychiatry Department, Geneva University Hospital and Faculty of Medicine, Geneva University Hospital, Geneva, Switzerland
- General Pediatric Division, Geneva University Hospital, Geneva, Switzerland
| | - Dimitri Van De Ville
- Neuro-X Institute, École Polytechnique Fédérale de Lausanne, Geneva, Switzerland
- Department of Radiology and Medical Informatics, University of Geneva, Geneva, Switzerland
| | - Giovanni Castellini
- Psychiatry Unit, Department of Health Sciences, University of Florence, Florence, Italy
| | - Valdo Ricca
- Psychiatry Unit, Department of Health Sciences, University of Florence, Florence, Italy
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Zhang C, Liang J, Yan H, Li X, Li X, Jing H, Liang W, Li R, Ou Y, Wu W, Guo H, Deng W, Xie G, Guo W. Fractional amplitude of low-frequency fluctuations in sensory-motor networks and limbic system as a potential predictor of treatment response in patients with schizophrenia. Schizophr Res 2024; 267:519-527. [PMID: 38704344 DOI: 10.1016/j.schres.2024.04.020] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/14/2023] [Revised: 03/21/2024] [Accepted: 04/26/2024] [Indexed: 05/06/2024]
Abstract
BACKGROUND Previous investigations have revealed substantial differences in neuroimaging characteristics between healthy controls (HCs) and individuals diagnosed with schizophrenia (SCZ). However, we are not entirely sure how brain activity links to symptoms in schizophrenia, and there is a need for reliable brain imaging markers for treatment prediction. METHODS In this longitudinal study, we examined 56 individuals diagnosed with 56 SCZ and 51 HCs. The SCZ patients underwent a three-month course of antipsychotic treatment. We employed resting-state functional magnetic resonance imaging (fMRI) along with fractional Amplitude of Low Frequency Fluctuations (fALFF) and support vector regression (SVR) methods for data acquisition and subsequent analysis. RESULTS In this study, we initially noted lower fALFF values in the right postcentral/precentral gyrus and left postcentral gyrus, coupled with higher fALFF values in the left hippocampus and right putamen in SCZ patients compared to the HCs at baseline. However, when comparing fALFF values in brain regions with abnormal baseline fALFF values for SCZ patients who completed the follow-up, no significant differences in fALFF values were observed after 3 months of treatment compared to baseline data. The fALFF values in the right postcentral/precentral gyrus and left postcentral gyrus, and the left postcentral gyrus were useful in predicting treatment effects. CONCLUSION Our findings suggest that reduced fALFF values in the sensory-motor networks and increased fALFF values in the limbic system may constitute distinctive neurobiological features in SCZ patients. These findings may serve as potential neuroimaging markers for the prognosis of SCZ patients.
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Affiliation(s)
- Chunguo Zhang
- Department of Psychiatry, The Third People's Hospital of Foshan, Foshan, Guangdong 528000, China
| | - Jiaquan Liang
- Department of Psychiatry, The Third People's Hospital of Foshan, Foshan, Guangdong 528000, China
| | - Haohao Yan
- Department of Psychiatry, National Clinical Research Center for Mental Disorders, and National Center for Mental Disorders, The Second Xiangya Hospital of Central South University, Changsha 410011, Hunan, China
| | - Xiaoling Li
- Department of Psychiatry, The Third People's Hospital of Foshan, Foshan, Guangdong 528000, China
| | - Xuesong Li
- Department of Psychiatry, The Third People's Hospital of Foshan, Foshan, Guangdong 528000, China
| | - Huan Jing
- Department of Psychiatry, The Third People's Hospital of Foshan, Foshan, Guangdong 528000, China
| | - Wenting Liang
- Department of Psychiatry, The Third People's Hospital of Foshan, Foshan, Guangdong 528000, China
| | - Rongwei Li
- Department of Psychiatry, The Third People's Hospital of Foshan, Foshan, Guangdong 528000, China
| | - Yangpan Ou
- Department of Psychiatry, National Clinical Research Center for Mental Disorders, and National Center for Mental Disorders, The Second Xiangya Hospital of Central South University, Changsha 410011, Hunan, China
| | - Weibin Wu
- Department of Psychiatry, The Third People's Hospital of Foshan, Foshan, Guangdong 528000, China
| | - Huagui Guo
- Department of Psychiatry, The Third People's Hospital of Foshan, Foshan, Guangdong 528000, China
| | - Wen Deng
- Department of Psychiatry, The Third People's Hospital of Foshan, Foshan, Guangdong 528000, China
| | - Guojun Xie
- Department of Psychiatry, The Third People's Hospital of Foshan, Foshan, Guangdong 528000, China.
| | - Wenbin Guo
- Department of Psychiatry, National Clinical Research Center for Mental Disorders, and National Center for Mental Disorders, The Second Xiangya Hospital of Central South University, Changsha 410011, Hunan, China.
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Kang Y, Zhang Y, Huang K, Wang Z. Association of dopamine-based genetic risk score with dynamic low-frequency fluctuations in first-episode drug-naïve schizophrenia. Brain Imaging Behav 2023; 17:584-594. [PMID: 37382826 DOI: 10.1007/s11682-023-00786-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 06/11/2023] [Indexed: 06/30/2023]
Abstract
Alterations in dynamic intrinsic brain activity and signaling of neurotransmitters, such as dopamine, have been independently detected in schizophrenia patients. Yet, it remains unclear whether the dopamine genetic risk variants have association with brain intrinsic activity. We aimed to investigate the schizophrenia-specific dynamic amplitude of low frequency fluctuation (dALFF) altered pattern, and its association with dopamine genetic risk score in first-episode drug-naïve schizophrenia (FES). Fifty-two FES and 51 healthy controls were included. A sliding-window method based on the dALFF was adopted to estimate the dynamic alterations in intrinsic brain activity. Subjects were genotyped, and a genetic risk score (GRS), which combined the additive effects of ten risk genotypes from five dopamine-related genes, was calculated. We used the voxel-wise correlation analysis to explore the association of dopamine-GRS with dALFF. FES showed significantly increased dALFF left medial prefrontal cortex and significantly decreased dALFF in the right posterior cingulate cortex compared with healthy controls. Greater dopamine GRS in FES was associated with higher dALFF in the left middle frontal gyrus and left inferior parietal gyrus. Our findings indicate that cumulative dopamine genetic risk is associated with a known imaging phenotype for schizophrenia.
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Affiliation(s)
- Yafei Kang
- Shaanxi Provincial Key Research Center of Child Mental and Behavioral Health, School of Psychology, Shaanxi Normal University, Xi'an, China
| | - Youming Zhang
- Department of Radiology, Xiangya Hospital, Central South University, Changsha, China
| | - Kexin Huang
- West China Biomedical Big Data Centre, West China Hospital, Sichuan University, Chengdu, Sichuan, China.
| | - Zhenhong Wang
- Shaanxi Provincial Key Research Center of Child Mental and Behavioral Health, School of Psychology, Shaanxi Normal University, Xi'an, China.
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Zarghami TS, Zeidman P, Razi A, Bahrami F, Hossein‐Zadeh G. Dysconnection and cognition in schizophrenia: A spectral dynamic causal modeling study. Hum Brain Mapp 2023; 44:2873-2896. [PMID: 36852654 PMCID: PMC10089110 DOI: 10.1002/hbm.26251] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/12/2022] [Revised: 01/28/2023] [Accepted: 02/13/2023] [Indexed: 03/01/2023] Open
Abstract
Schizophrenia (SZ) is a severe mental disorder characterized by failure of functional integration (aka dysconnection) across the brain. Recent functional connectivity (FC) studies have adopted functional parcellations to define subnetworks of large-scale networks, and to characterize the (dys)connection between them, in normal and clinical populations. While FC examines statistical dependencies between observations, model-based effective connectivity (EC) can disclose the causal influences that underwrite the observed dependencies. In this study, we investigated resting state EC within seven large-scale networks, in 66 SZ and 74 healthy subjects from a public dataset. The results showed that a remarkable 33% of the effective connections (among subnetworks) of the cognitive control network had been pathologically modulated in SZ. Further dysconnection was identified within the visual, default mode and sensorimotor networks of SZ subjects, with 24%, 20%, and 11% aberrant couplings. Overall, the proportion of discriminative connections was remarkably larger in EC (24%) than FC (1%) analysis. Subsequently, to study the neural correlates of impaired cognition in SZ, we conducted a canonical correlation analysis between the EC parameters and the cognitive scores of the patients. As such, the self-inhibitions of supplementary motor area and paracentral lobule (in the sensorimotor network) and the excitatory connection from parahippocampal gyrus to inferior temporal gyrus (in the cognitive control network) were significantly correlated with the social cognition, reasoning/problem solving and working memory capabilities of the patients. Future research can investigate the potential of whole-brain EC as a biomarker for diagnosis of brain disorders and for neuroimaging-based cognitive assessment.
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Affiliation(s)
- Tahereh S. Zarghami
- Bio‐Electric Department, School of Electrical and Computer Engineering, College of EngineeringUniversity of TeranTehranIran
- Human Motor Control and Computational Neuroscience Laboratory, School of Electrical and Computer Engineering, College of EngineeringUniversity of TehranTehranIran
| | - Peter Zeidman
- The Wellcome Centre for Human NeuroimagingUniversity College LondonLondonUK
| | - Adeel Razi
- The Wellcome Centre for Human NeuroimagingUniversity College LondonLondonUK
- Turner Institute for Brain and Mental HealthMonash UniversityClaytonVictoriaAustralia
- Monash Biomedical ImagingMonash UniversityClaytonVictoriaAustralia
- CIFAR Azrieli Global Scholars Program, CIFARTorontoCanada
| | - Fariba Bahrami
- Bio‐Electric Department, School of Electrical and Computer Engineering, College of EngineeringUniversity of TeranTehranIran
- Human Motor Control and Computational Neuroscience Laboratory, School of Electrical and Computer Engineering, College of EngineeringUniversity of TehranTehranIran
| | - Gholam‐Ali Hossein‐Zadeh
- Bio‐Electric Department, School of Electrical and Computer Engineering, College of EngineeringUniversity of TeranTehranIran
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Rong B, Huang H, Gao G, Sun L, Zhou Y, Xiao L, Wang H, Wang G. Widespread Intra- and Inter-Network Dysconnectivity among Large-Scale Resting State Networks in Schizophrenia. J Clin Med 2023; 12:jcm12093176. [PMID: 37176617 PMCID: PMC10179370 DOI: 10.3390/jcm12093176] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/27/2023] [Revised: 03/08/2023] [Accepted: 04/07/2023] [Indexed: 05/15/2023] Open
Abstract
Schizophrenia is characterized by the distributed dysconnectivity of resting-state multiple brain networks. However, the abnormalities of intra- and inter-network functional connectivity (FC) in schizophrenia and its relationship to symptoms remain unknown. The aim of the present study is to compare the intra- and inter-connectivity of the intrinsic networks between a large sample of patients with schizophrenia and healthy controls. Using the Region of interest (ROI) to ROI FC analyses, the intra- and inter-network FC of the eight resting state networks [default mode network (DMN); salience network (SN); frontoparietal network (FPN); dorsal attention network (DAN); language network (LN); visual network (VN); sensorimotor network (SMN); and cerebellar network (CN)] were investigated in 196 schizophrenia and 169-healthy controls. Compared to the healthy control group, the schizophrenia group exhibited increased intra-network FC in the DMN and decreased intra-network FC in the CN. Additionally, the schizophrenia group showed the decreased inter-network FC mainly involved the SN-DMN, SN-LN and SN-CN while increased inter-network FC in the SN-SMN and SN-DAN (p < 0.05, FDR-corrected). Our study suggests widespread intra- and inter-network dysconnectivity among large-scale RSNs in schizophrenia, mainly involving the DMN, SN and SMN, which may further contribute to the dysconnectivity hypothesis of schizophrenia.
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Affiliation(s)
- Bei Rong
- Department of Psychiatry, Renmin Hospital of Wuhan University, Wuhan 430060, China
- Institute of Neuropsychiatry, Renmin Hospital of Wuhan University, Wuhan 430060, China
| | - Huan Huang
- Department of Psychiatry, Renmin Hospital of Wuhan University, Wuhan 430060, China
| | - Guoqing Gao
- Department of Psychiatry, Renmin Hospital of Wuhan University, Wuhan 430060, China
| | - Limin Sun
- Department of Psychiatry, Renmin Hospital of Wuhan University, Wuhan 430060, China
- Institute of Neuropsychiatry, Renmin Hospital of Wuhan University, Wuhan 430060, China
| | - Yuan Zhou
- Institute of Psychology, CAS Key Laboratory of Behavioral Science, Beijing 100101, China
| | - Ling Xiao
- Department of Psychiatry, Renmin Hospital of Wuhan University, Wuhan 430060, China
- Institute of Neuropsychiatry, Renmin Hospital of Wuhan University, Wuhan 430060, China
| | - Huiling Wang
- Department of Psychiatry, Renmin Hospital of Wuhan University, Wuhan 430060, China
| | - Gaohua Wang
- Department of Psychiatry, Renmin Hospital of Wuhan University, Wuhan 430060, China
- Institute of Neuropsychiatry, Renmin Hospital of Wuhan University, Wuhan 430060, China
- Taikang Center for Life and Medical Sciences, Wuhan University, Wuhan 430071, China
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Dong F, Zhang Z, Chu T, Che K, Li Y, Gai Q, Shi Y, Ma H, Zhao F, Mao N, Xie H. Altered dynamic amplitude of low-frequency fluctuations in patients with postpartum depression. Behav Brain Res 2022; 433:113980. [PMID: 35809693 DOI: 10.1016/j.bbr.2022.113980] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/19/2022] [Revised: 06/21/2022] [Accepted: 06/22/2022] [Indexed: 11/17/2022]
Abstract
BACKGROUND Postpartum depression (PPD) is a common mood disorder with increasing incidence year by year. However, the dynamic changes in local neural activity of patients with PPD remain unclear. In this study, we utilized the dynamic amplitude of low-frequency fluctuation (dALFF) method to investigate the abnormal temporal variability of local neural activity and its potential correlation with clinical severity in PPD. METHODS Twenty-four patients with PPD and nineteen healthy primiparous mothers controls (HCs) matched for age, education level and body mass index were examined by resting-state functional magnetic resonance imaging (rs-fMRI). A sliding-window method was used to assess the dALFF, and a k-means clustering method was used to identify dALFF states. Two-sample t-test was used to compare the differences of dALFF variability and state metrics between PPD and HCs. Pearson correlation analysis was used to analyze the relationship between dALFF variability, states metrics and clinical severity. RESULTS (1) Patients with PPD had lower variance of dALFF than HCs in the cognitive control network, cerebellar network and sensorimotor network. (2) Four dALFF states were identified, and patients with PPD spent more time on state 2 than the other three states. The number of transitions between the four dALFF states increased in the patients compared with that in HCs. (3) Multiple dALFF states were found to be correlated with the severity of depression. The variance of dALFF in the right middle frontal gyrus was negatively correlated with the Edinburgh postnatal depression scale score. CONCLUSION This study provides new insights into the brain dysfunction of PPD from the perspective of dynamic local brain activity, highlighting the important role of dALFF variability in understanding the neurophysiological mechanisms of PPD.
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Affiliation(s)
- Fanghui Dong
- School of Medical Imaging, Binzhou Medical University, No. 346 Guanhai Road, Yantai, Shandong 264003, PR China; Department of Radiology, Yantai Yuhuangding Hospital, Qingdao University, Yantai, Shandong 264000, PR China
| | - Zhongsheng Zhang
- Department of Radiology, Yantai Yuhuangding Hospital, Qingdao University, Yantai, Shandong 264000, PR China
| | - Tongpeng Chu
- Department of Radiology, Yantai Yuhuangding Hospital, Qingdao University, Yantai, Shandong 264000, PR China
| | - Kaili Che
- Department of Radiology, Yantai Yuhuangding Hospital, Qingdao University, Yantai, Shandong 264000, PR China
| | - Yuna Li
- Department of Radiology, Yantai Yuhuangding Hospital, Qingdao University, Yantai, Shandong 264000, PR China
| | - Qun Gai
- Department of Radiology, Yantai Yuhuangding Hospital, Qingdao University, Yantai, Shandong 264000, PR China
| | - Yinghong Shi
- Department of Radiology, Yantai Yuhuangding Hospital, Qingdao University, Yantai, Shandong 264000, PR China
| | - Heng Ma
- Department of Radiology, Yantai Yuhuangding Hospital, Qingdao University, Yantai, Shandong 264000, PR China
| | - Feng Zhao
- School of Compute Science and Technology, Shandong Technology and Business University, Yantai, Shandong 264000, PR China
| | - Ning Mao
- Department of Radiology, Yantai Yuhuangding Hospital, Qingdao University, Yantai, Shandong 264000, PR China.
| | - Haizhu Xie
- School of Medical Imaging, Binzhou Medical University, No. 346 Guanhai Road, Yantai, Shandong 264003, PR China; Department of Radiology, Yantai Yuhuangding Hospital, Qingdao University, Yantai, Shandong 264000, PR China.
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