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Mana L, Schwartz-Pallejà M, Vila-Vidal M, Deco G. Overview on cognitive impairment in psychotic disorders: From impaired microcircuits to dysconnectivity. Schizophr Res 2024; 269:132-143. [PMID: 38788432 DOI: 10.1016/j.schres.2024.05.008] [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: 01/15/2024] [Revised: 05/09/2024] [Accepted: 05/13/2024] [Indexed: 05/26/2024]
Abstract
Schizophrenia's cognitive deficits, often overshadowed by positive symptoms, significantly contribute to the disorder's morbidity. Increasing attention highlights these deficits as reflections of neural circuit dysfunction across various cortical regions. Numerous connectivity alterations linked to cognitive symptoms in psychotic disorders have been reported, both at the macroscopic and microscopic level, emphasizing the potential role of plasticity and microcircuits impairment during development and later stages. However, the heterogeneous clinical presentation of cognitive impairment and diverse connectivity findings pose challenges in summarizing them into a cohesive picture. This review aims to synthesize major cognitive alterations, recent insights into network structural and functional connectivity changes and proposed mechanisms and microcircuit alterations underpinning these symptoms, particularly focusing on neurodevelopmental impairment, E/I balance, and sleep disturbances. Finally, we will also comment on some of the most recent and promising therapeutic approaches that aim to target these mechanisms to address cognitive symptoms. Through this comprehensive exploration, we strive to provide an updated and nuanced overview of the multiscale connectivity impairment underlying cognitive impairment in psychotic disorders.
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Affiliation(s)
- L Mana
- Center for Brain and Cognition, Computational Neuroscience Group, Department of Information and Communication Technologies, Universitat Pompeu Fabra, Roc Boronat 138, Barcelona 08018, Spain.
| | - M Schwartz-Pallejà
- Center for Brain and Cognition, Computational Neuroscience Group, Department of Information and Communication Technologies, Universitat Pompeu Fabra, Roc Boronat 138, Barcelona 08018, Spain; Department of Experimental and Health Science, Universitat Pompeu Fabra, Roc Boronat 138, Barcelona 08018, Spain; Eurecat, Technology Center of Catalonia, Multimedia Technologies, Barcelona, Spain.
| | - M Vila-Vidal
- Center for Brain and Cognition, Computational Neuroscience Group, Department of Information and Communication Technologies, Universitat Pompeu Fabra, Roc Boronat 138, Barcelona 08018, Spain; Computational Biology and Complex Systems Group, Department of Physics, Universitat Politècnica de Catalunya, Barcelona, Spain.
| | - G Deco
- Center for Brain and Cognition, Computational Neuroscience Group, Department of Information and Communication Technologies, Universitat Pompeu Fabra, Roc Boronat 138, Barcelona 08018, Spain; Institució Catalana de la Recerca i Estudis Avançats (ICREA), Passeig Lluís Companys 23, Barcelona 08010, Spain.
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Wen J, Antoniades M, Yang Z, Hwang G, Skampardoni I, Wang R, Davatzikos C. Dimensional Neuroimaging Endophenotypes: Neurobiological Representations of Disease Heterogeneity Through Machine Learning. Biol Psychiatry 2024:S0006-3223(24)01286-1. [PMID: 38718880 DOI: 10.1016/j.biopsych.2024.04.017] [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: 01/17/2024] [Revised: 03/29/2024] [Accepted: 04/22/2024] [Indexed: 05/21/2024]
Abstract
Machine learning has been increasingly used to obtain individualized neuroimaging signatures for disease diagnosis, prognosis, and response to treatment in neuropsychiatric and neurodegenerative disorders. Therefore, it has contributed to a better understanding of disease heterogeneity by identifying disease subtypes with different brain phenotypic measures. In this Review, we first present a systematic literature overview of studies using machine learning and multimodal MRI to unravel disease heterogeneity in various neuropsychiatric and neurodegenerative disorders, including Alzheimer's disease, schizophrenia, major depressive disorder, autism spectrum disorder, multiple sclerosis, as well as their potential in a transdiagnostic framework, where neuroanatomical and neurobiological commonalities were assessed across diagnostic boundaries. Subsequently, we summarize relevant machine learning methodologies and their clinical interpretability. We discuss the potential clinical implications of the current findings and envision future research avenues. Finally, we discuss an emerging paradigm called dimensional neuroimaging endophenotypes (DNE). DNE dissects the neurobiological heterogeneity of neuropsychiatric and neurodegenerative disorders into low-dimensional yet informative, quantitative brain phenotypic representations, serving as robust intermediate phenotypes (i.e., endophenotypes) presumably reflecting the interplay of underlying genetic, lifestyle, and environmental processes associated with the disease etiology.
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Affiliation(s)
- Junhao Wen
- Laboratory of AI and Biomedical Science (LABS), Stevens Neuroimaging and Informatics Institute, Keck School of Medicine of USC, University of Southern California, Los Angeles, CA, USA.
| | - Mathilde Antoniades
- Artificial Intelligence in Biomedical Imaging Laboratory (AIBIL), Center for AI and Data Science for Integrated Diagnostics (AI(2)D), Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Zhijian Yang
- Artificial Intelligence in Biomedical Imaging Laboratory (AIBIL), Center for AI and Data Science for Integrated Diagnostics (AI(2)D), Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Gyujoon Hwang
- Psychiatry and Behavioral Medicine, Medical College of Wisconsin, Watertown Plank Rd, Milwaukee, WI, USA
| | - Ioanna Skampardoni
- Artificial Intelligence in Biomedical Imaging Laboratory (AIBIL), Center for AI and Data Science for Integrated Diagnostics (AI(2)D), Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Rongguang Wang
- Artificial Intelligence in Biomedical Imaging Laboratory (AIBIL), Center for AI and Data Science for Integrated Diagnostics (AI(2)D), Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Christos Davatzikos
- Artificial Intelligence in Biomedical Imaging Laboratory (AIBIL), Center for AI and Data Science for Integrated Diagnostics (AI(2)D), Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA.
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Zhang Y, Yin X, Chen YC, Chen H, Jin M, Ma Y, Yong W, Muthaiah VPK, Xia W, Yin X. Aberrant Brain Triple-Network Effective Connectivity Patterns in Type 2 Diabetes Mellitus. Diabetes Ther 2024; 15:1215-1229. [PMID: 38578396 PMCID: PMC11043308 DOI: 10.1007/s13300-024-01565-y] [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: 01/30/2024] [Accepted: 03/12/2024] [Indexed: 04/06/2024] Open
Abstract
INTRODUCTION Aberrant brain functional connectivity network is thought to be related to cognitive impairment in patients with type 2 diabetes mellitus (T2DM). This study aims to investigate the triple-network effective connectivity patterns in patients with T2DM within and between the default mode network (DMN), salience network (SN), and executive control network (ECN) and their associations with cognitive declines. METHODS In total, 92 patients with T2DM and 98 matched healthy controls (HCs) were recruited and underwent resting-state functional magnetic resonance imaging (rs-fMRI). Spectral dynamic causal modeling (spDCM) was used for effective connectivity analysis within the triple network. The posterior cingulate cortex (PCC), medial prefrontal cortex (mPFC), lateral prefrontal cortex (LPFC), supramarginal gyrus (SMG), and anterior insula (AINS) were selected as the regions of interest. Group comparisons were performed for effective connectivity calculated using the fully connected model, and the relationships between effective connectivity alterations and cognitive impairment as well as clinical parameters were detected. RESULTS Compared to HCs, patients with T2DM exhibited increased or decreased effective connectivity patterns within the triple network. Furthermore, diabetes duration was significantly negatively correlated with increased effective connectivity from the r-LPFC to the mPFC, while body mass index (BMI) was significantly positively correlated with increased effective connectivity from the l-LPFC to the l-AINS (r = - 0.353, p = 0.001; r = 0.377, p = 0.004). CONCLUSION These results indicate abnormal effective connectivity patterns within the triple network model in patients with T2DM and provide new insight into the neurological mechanisms of T2DM and related cognitive dysfunction.
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Affiliation(s)
- Yujie Zhang
- Department of Radiology, Nanjing First Hospital, Nanjing Medical University, No.68, Changle Road, Nanjing, 210006, China
| | - Xiao Yin
- Department of Endocrinology, Nanjing First Hospital, Nanjing Medical University, No.68, Changle Road, Nanjing, 210006, China
| | - Yu-Chen Chen
- Department of Radiology, Nanjing First Hospital, Nanjing Medical University, No.68, Changle Road, Nanjing, 210006, China
| | - Huiyou Chen
- Department of Radiology, Nanjing First Hospital, Nanjing Medical University, No.68, Changle Road, Nanjing, 210006, China
| | - Mingxu Jin
- Department of Radiology, Nanjing First Hospital, Nanjing Medical University, No.68, Changle Road, Nanjing, 210006, China
| | - Yuehu Ma
- Department of Radiology, Nanjing First Hospital, Nanjing Medical University, No.68, Changle Road, Nanjing, 210006, China
| | - Wei Yong
- Department of Radiology, Nanjing First Hospital, Nanjing Medical University, No.68, Changle Road, Nanjing, 210006, China
| | | | - Wenqing Xia
- Department of Endocrinology, Nanjing First Hospital, Nanjing Medical University, No.68, Changle Road, Nanjing, 210006, China.
| | - Xindao Yin
- Department of Radiology, Nanjing First Hospital, Nanjing Medical University, No.68, Changle Road, Nanjing, 210006, China.
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Mehta UM, Ithal D, Roy N, Shekhar S, Govindaraj R, Ramachandraiah CT, Bolo NR, Bharath RD, Thirthalli J, Venkatasubramanian G, Gangadhar BN, Keshavan MS. Posterior Cerebellar Resting-State Functional Hypoconnectivity: A Neural Marker of Schizophrenia Across Different Stages of Treatment Response. Biol Psychiatry 2024:S0006-3223(24)00076-3. [PMID: 38336217 DOI: 10.1016/j.biopsych.2024.01.027] [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: 08/02/2023] [Revised: 01/11/2024] [Accepted: 01/30/2024] [Indexed: 02/12/2024]
Abstract
BACKGROUND Identifying stable and consistent resting-state functional connectivity patterns across illness trajectories has the potential to be considered fundamental to the pathophysiology of schizophrenia. We aimed to identify consistent resting-state functional connectivity patterns across heterogeneous schizophrenia groups defined based on treatment response. METHODS In phase 1, we used a cross-sectional case-control design to characterize and compare stable independent component networks from resting-state functional magnetic resonance imaging scans of antipsychotic-naïve participants with first-episode schizophrenia (n = 54) and healthy participants (n = 43); we also examined associations with symptoms, cognition, and disability. In phase 2, we examined the stability (and replicability) of our phase 1 results in 4 groups (N = 105) representing a cross-sequential gradation of schizophrenia based on treatment response: risperidone responders, clozapine responders, clozapine nonresponders, and clozapine nonresponders following electroconvulsive therapy. Hypothesis-free whole-brain within- and between-network connectivity were examined. RESULTS Phase 1 identified posterior and anterior cerebellar hypoconnectivity and limbic hyperconnectivity in schizophrenia at a familywise error rate-corrected cluster significance threshold of p < .01. These network aberrations had unique associations with positive symptoms, cognition, and disability. During phase 2, we replicated the phase 1 results while comparing each of the 4 schizophrenia groups to the healthy participants. The participants in 2 longitudinal subdatasets did not demonstrate a significant change in these network aberrations following risperidone or electroconvulsive therapy. Posterior cerebellar hypoconnectivity (with thalamus and cingulate) emerged as the most consistent finding; it was replicated across different stages of treatment response (Cohen's d range -0.95 to -1.44), reproduced using different preprocessing techniques, and not confounded by educational attainment. CONCLUSIONS Posterior cerebellar-thalamo-cingulate hypoconnectivity is a consistent and stable state-independent neural marker of schizophrenia.
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Affiliation(s)
- Urvakhsh Meherwan Mehta
- Department of Psychiatry, National Institute of Mental Health & Neurosciences, Bangalore, India.
| | - Dhruva Ithal
- Department of Psychiatry, National Institute of Mental Health & Neurosciences, Bangalore, India
| | - Neelabja Roy
- Department of Psychiatry, National Institute of Mental Health & Neurosciences, Bangalore, India
| | - Shreshth Shekhar
- Department of Psychiatry, National Institute of Mental Health & Neurosciences, Bangalore, India
| | - Ramajayam Govindaraj
- Department of Psychiatry, National Institute of Mental Health & Neurosciences, Bangalore, India
| | | | - Nicolas R Bolo
- Beth Israel Deaconess Medical Center and Massachusetts Mental Health Center, Harvard Medical School, Boston, Massachusetts
| | - Rose Dawn Bharath
- Department of Neuroimaging & Interventional Radiology, National Institute of Mental Health & Neurosciences, Bangalore, India
| | - Jagadisha Thirthalli
- Department of Psychiatry, National Institute of Mental Health & Neurosciences, Bangalore, India
| | | | - Bangalore N Gangadhar
- Department of Psychiatry, National Institute of Mental Health & Neurosciences, Bangalore, India
| | - Matcheri S Keshavan
- Beth Israel Deaconess Medical Center and Massachusetts Mental Health Center, Harvard Medical School, Boston, Massachusetts
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Voineskos AN, Hawco C, Neufeld NH, Turner JA, Ameis SH, Anticevic A, Buchanan RW, Cadenhead K, Dazzan P, Dickie EW, Gallucci J, Lahti AC, Malhotra AK, Öngür D, Lencz T, Sarpal DK, Oliver LD. Functional magnetic resonance imaging in schizophrenia: current evidence, methodological advances, limitations and future directions. World Psychiatry 2024; 23:26-51. [PMID: 38214624 PMCID: PMC10786022 DOI: 10.1002/wps.21159] [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] [Indexed: 01/13/2024] Open
Abstract
Functional neuroimaging emerged with great promise and has provided fundamental insights into the neurobiology of schizophrenia. However, it has faced challenges and criticisms, most notably a lack of clinical translation. This paper provides a comprehensive review and critical summary of the literature on functional neuroimaging, in particular functional magnetic resonance imaging (fMRI), in schizophrenia. We begin by reviewing research on fMRI biomarkers in schizophrenia and the clinical high risk phase through a historical lens, moving from case-control regional brain activation to global connectivity and advanced analytical approaches, and more recent machine learning algorithms to identify predictive neuroimaging features. Findings from fMRI studies of negative symptoms as well as of neurocognitive and social cognitive deficits are then reviewed. Functional neural markers of these symptoms and deficits may represent promising treatment targets in schizophrenia. Next, we summarize fMRI research related to antipsychotic medication, psychotherapy and psychosocial interventions, and neurostimulation, including treatment response and resistance, therapeutic mechanisms, and treatment targeting. We also review the utility of fMRI and data-driven approaches to dissect the heterogeneity of schizophrenia, moving beyond case-control comparisons, as well as methodological considerations and advances, including consortia and precision fMRI. Lastly, limitations and future directions of research in the field are discussed. Our comprehensive review suggests that, in order for fMRI to be clinically useful in the care of patients with schizophrenia, research should address potentially actionable clinical decisions that are routine in schizophrenia treatment, such as which antipsychotic should be prescribed or whether a given patient is likely to have persistent functional impairment. The potential clinical utility of fMRI is influenced by and must be weighed against cost and accessibility factors. Future evaluations of the utility of fMRI in prognostic and treatment response studies may consider including a health economics analysis.
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Affiliation(s)
- Aristotle N Voineskos
- Campbell Family Mental Health Research Institute and Brain Health Imaging Centre, Centre for Addiction and Mental Health, Toronto, ON, Canada
- Department of Psychiatry, Temerty Faculty of Medicine, University of Toronto, Toronto, ON, Canada
| | - Colin Hawco
- Campbell Family Mental Health Research Institute and Brain Health Imaging Centre, Centre for Addiction and Mental Health, Toronto, ON, Canada
- Department of Psychiatry, Temerty Faculty of Medicine, University of Toronto, Toronto, ON, Canada
| | - Nicholas H Neufeld
- Campbell Family Mental Health Research Institute and Brain Health Imaging Centre, Centre for Addiction and Mental Health, Toronto, ON, Canada
- Department of Psychiatry, Temerty Faculty of Medicine, University of Toronto, Toronto, ON, Canada
| | - Jessica A Turner
- Department of Psychiatry and Behavioral Health, Wexner Medical Center, Ohio State University, Columbus, OH, USA
| | - Stephanie H Ameis
- Campbell Family Mental Health Research Institute and Brain Health Imaging Centre, Centre for Addiction and Mental Health, Toronto, ON, Canada
- Department of Psychiatry, Temerty Faculty of Medicine, University of Toronto, Toronto, ON, Canada
- Cundill Centre for Child and Youth Depression and McCain Centre for Child, Youth and Family Mental Health, Centre for Addiction and Mental Health, Toronto, ON, Canada
| | - Alan Anticevic
- Interdepartmental Neuroscience Program, Yale University, New Haven, CT, USA
- Department of Psychiatry, Yale University, New Haven, CT, USA
| | - Robert W Buchanan
- Maryland Psychiatric Research Center, Department of Psychiatry, University of Maryland School of Medicine, Baltimore, MD, USA
| | - Kristin Cadenhead
- Department of Psychiatry, University of California San Diego, La Jolla, CA, USA
| | - Paola Dazzan
- Department of Psychological Medicine, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, UK
| | - Erin W Dickie
- Campbell Family Mental Health Research Institute and Brain Health Imaging Centre, Centre for Addiction and Mental Health, Toronto, ON, Canada
- Department of Psychiatry, Temerty Faculty of Medicine, University of Toronto, Toronto, ON, Canada
| | - Julia Gallucci
- Campbell Family Mental Health Research Institute and Brain Health Imaging Centre, Centre for Addiction and Mental Health, Toronto, ON, Canada
- Institute of Medical Science, University of Toronto, Toronto, ON, Canada
| | - Adrienne C Lahti
- Department of Psychiatry and Behavioral Neurobiology, University of Alabama at Birmingham, Birmingham, AL, USA
| | - Anil K Malhotra
- Institute for Behavioral Science, Feinstein Institutes for Medical Research, Manhasset, NY, USA
- Department of Psychiatry, Zucker School of Medicine at Hofstra/Northwell, Hempstead, NY, USA
- Department of Molecular Medicine, Zucker School of Medicine at Hofstra/Northwell, Hempstead, NY, USA
- Department of Psychiatry, Zucker Hillside Hospital Division of Northwell Health, Glen Oaks, NY, USA
| | - Dost Öngür
- McLean Hospital/Harvard Medical School, Belmont, MA, USA
| | - Todd Lencz
- Institute for Behavioral Science, Feinstein Institutes for Medical Research, Manhasset, NY, USA
- Department of Psychiatry, Zucker School of Medicine at Hofstra/Northwell, Hempstead, NY, USA
- Department of Molecular Medicine, Zucker School of Medicine at Hofstra/Northwell, Hempstead, NY, USA
- Department of Psychiatry, Zucker Hillside Hospital Division of Northwell Health, Glen Oaks, NY, USA
| | - Deepak K Sarpal
- Department of Psychiatry, School of Medicine, University of Pittsburgh, Pittsburgh, PA, USA
| | - Lindsay D Oliver
- Campbell Family Mental Health Research Institute and Brain Health Imaging Centre, Centre for Addiction and Mental Health, Toronto, ON, Canada
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Wen J, Antoniades M, Yang Z, Hwang G, Skampardoni I, Wang R, Davatzikos C. Dimensional Neuroimaging Endophenotypes: Neurobiological Representations of Disease Heterogeneity Through Machine Learning. ARXIV 2024:arXiv:2401.09517v1. [PMID: 38313197 PMCID: PMC10836087] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Subscribe] [Scholar Register] [Indexed: 02/06/2024]
Abstract
Machine learning has been increasingly used to obtain individualized neuroimaging signatures for disease diagnosis, prognosis, and response to treatment in neuropsychiatric and neurodegenerative disorders. Therefore, it has contributed to a better understanding of disease heterogeneity by identifying disease subtypes that present significant differences in various brain phenotypic measures. In this review, we first present a systematic literature overview of studies using machine learning and multimodal MRI to unravel disease heterogeneity in various neuropsychiatric and neurodegenerative disorders, including Alzheimer's disease, schizophrenia, major depressive disorder, autism spectrum disorder, multiple sclerosis, as well as their potential in transdiagnostic settings. Subsequently, we summarize relevant machine learning methodologies and discuss an emerging paradigm which we call dimensional neuroimaging endophenotype (DNE). DNE dissects the neurobiological heterogeneity of neuropsychiatric and neurodegenerative disorders into a low-dimensional yet informative, quantitative brain phenotypic representation, serving as a robust intermediate phenotype (i.e., endophenotype) largely reflecting underlying genetics and etiology. Finally, we discuss the potential clinical implications of the current findings and envision future research avenues.
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Affiliation(s)
- Junhao Wen
- Laboratory of AI and Biomedical Science (LABS), Stevens Neuroimaging and Informatics Institute, Keck School of Medicine of USC, University of Southern California, Los Angeles, CA, USA
| | - Mathilde Antoniades
- Artificial Intelligence in Biomedical Imaging Laboratory (AIBIL), Center for AI and Data Science for Integrated Diagnostics (AID), Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Zhijian Yang
- Artificial Intelligence in Biomedical Imaging Laboratory (AIBIL), Center for AI and Data Science for Integrated Diagnostics (AID), Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Gyujoon Hwang
- Psychiatry and Behavioral Medicine, Medical College of Wisconsin, Watertown Plank Rd, Milwaukee, WI, USA
| | - Ioanna Skampardoni
- Artificial Intelligence in Biomedical Imaging Laboratory (AIBIL), Center for AI and Data Science for Integrated Diagnostics (AID), Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Rongguang Wang
- Artificial Intelligence in Biomedical Imaging Laboratory (AIBIL), Center for AI and Data Science for Integrated Diagnostics (AID), Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Christos Davatzikos
- Artificial Intelligence in Biomedical Imaging Laboratory (AIBIL), Center for AI and Data Science for Integrated Diagnostics (AID), Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
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Ambrosen KS, Fredriksson F, Anhøj S, Bak N, van Dellen E, Dominicus L, Lemvigh CK, Sørensen ME, Nielsen MØ, Bojesen KB, Fagerlund B, Glenthøj BY, Oranje B, Hansen LK, Ebdrup BH. Clustering of antipsychotic-naïve patients with schizophrenia based on functional connectivity from resting-state electroencephalography. Eur Arch Psychiatry Clin Neurosci 2023; 273:1785-1796. [PMID: 36729135 PMCID: PMC10713774 DOI: 10.1007/s00406-023-01550-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/11/2022] [Accepted: 01/09/2023] [Indexed: 02/03/2023]
Abstract
Schizophrenia is associated with aberrations in the Default Mode Network (DMN), but the clinical implications remain unclear. We applied data-driven, unsupervised machine learning based on resting-state electroencephalography (rsEEG) functional connectivity within the DMN to cluster antipsychotic-naïve patients with first-episode schizophrenia. The identified clusters were investigated with respect to psychopathological profile and cognitive deficits. Thirty-seven antipsychotic-naïve, first-episode patients with schizophrenia (mean age 24.4 (5.4); 59.5% males) and 97 matched healthy controls (mean age 24.0 (5.1); 52.6% males) underwent assessments of rsEEG, psychopathology, and cognition. Source-localized, frequency-dependent functional connectivity was estimated using Phase Lag Index (PLI). The DMN-PLI was factorized for each frequency band using principal component analysis. Clusters of patients were identified using a Gaussian mixture model and neurocognitive and psychopathological profiles of identified clusters were explored. We identified two clusters of patients based on the theta band (4-8 Hz), and two clusters based on the beta band (12-30 Hz). Baseline psychopathology could predict theta clusters with an accuracy of 69.4% (p = 0.003), primarily driven by negative symptoms. Five a priori selected cognitive functions conjointly predicted the beta clusters with an accuracy of 63.6% (p = 0.034). The two beta clusters displayed higher and lower DMN connectivity, respectively, compared to healthy controls. In conclusion, the functional connectivity within the DMN provides a novel, data-driven means to stratify patients into clinically relevant clusters. The results support the notion of biological subgroups in schizophrenia and endorse the application of data-driven methods to recognize pathophysiological patterns at earliest stage of this syndrome.
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Affiliation(s)
- Karen S Ambrosen
- Center for Neuropsychiatric Schizophrenia Research (CNSR) and Center for Clinical Intervention and Neuropsychiatric Schizophrenia Research (CINS), Copenhagen University Hospital, Mental Health Services CPH, Nordstjernevej 41, 2600, Glostrup, Denmark.
| | - Fanny Fredriksson
- Center for Neuropsychiatric Schizophrenia Research (CNSR) and Center for Clinical Intervention and Neuropsychiatric Schizophrenia Research (CINS), Copenhagen University Hospital, Mental Health Services CPH, Nordstjernevej 41, 2600, Glostrup, Denmark
| | - Simon Anhøj
- Center for Neuropsychiatric Schizophrenia Research (CNSR) and Center for Clinical Intervention and Neuropsychiatric Schizophrenia Research (CINS), Copenhagen University Hospital, Mental Health Services CPH, Nordstjernevej 41, 2600, Glostrup, Denmark
| | | | - Edwin van Dellen
- Department of Psychiatry, University Medical Center Utrecht, Brain Center Rudolf Magnus, Utrecht, The Netherlands
| | - Livia Dominicus
- Department of Psychiatry, University Medical Center Utrecht, Brain Center Rudolf Magnus, Utrecht, The Netherlands
| | - Cecilie K Lemvigh
- Center for Neuropsychiatric Schizophrenia Research (CNSR) and Center for Clinical Intervention and Neuropsychiatric Schizophrenia Research (CINS), Copenhagen University Hospital, Mental Health Services CPH, Nordstjernevej 41, 2600, Glostrup, Denmark
| | - Mikkel E Sørensen
- Center for Neuropsychiatric Schizophrenia Research (CNSR) and Center for Clinical Intervention and Neuropsychiatric Schizophrenia Research (CINS), Copenhagen University Hospital, Mental Health Services CPH, Nordstjernevej 41, 2600, Glostrup, Denmark
| | - Mette Ø Nielsen
- Center for Neuropsychiatric Schizophrenia Research (CNSR) and Center for Clinical Intervention and Neuropsychiatric Schizophrenia Research (CINS), Copenhagen University Hospital, Mental Health Services CPH, Nordstjernevej 41, 2600, Glostrup, Denmark
- Department of Clinical Medicine, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark
| | - Kirsten B Bojesen
- Center for Neuropsychiatric Schizophrenia Research (CNSR) and Center for Clinical Intervention and Neuropsychiatric Schizophrenia Research (CINS), Copenhagen University Hospital, Mental Health Services CPH, Nordstjernevej 41, 2600, Glostrup, Denmark
| | - Birgitte Fagerlund
- Center for Neuropsychiatric Schizophrenia Research (CNSR) and Center for Clinical Intervention and Neuropsychiatric Schizophrenia Research (CINS), Copenhagen University Hospital, Mental Health Services CPH, Nordstjernevej 41, 2600, Glostrup, Denmark
- Department of Psychology, University of Copenhagen, Copenhagen, Denmark
| | - Birte Y Glenthøj
- Center for Neuropsychiatric Schizophrenia Research (CNSR) and Center for Clinical Intervention and Neuropsychiatric Schizophrenia Research (CINS), Copenhagen University Hospital, Mental Health Services CPH, Nordstjernevej 41, 2600, Glostrup, Denmark
- Department of Clinical Medicine, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark
| | - Bob Oranje
- Center for Neuropsychiatric Schizophrenia Research (CNSR) and Center for Clinical Intervention and Neuropsychiatric Schizophrenia Research (CINS), Copenhagen University Hospital, Mental Health Services CPH, Nordstjernevej 41, 2600, Glostrup, Denmark
| | - Lars K Hansen
- Department of Applied Mathematics and Computer Science, DTU Compute, Technical University of Denmark, Kgs. Lyngby, Denmark
| | - Bjørn H Ebdrup
- Center for Neuropsychiatric Schizophrenia Research (CNSR) and Center for Clinical Intervention and Neuropsychiatric Schizophrenia Research (CINS), Copenhagen University Hospital, Mental Health Services CPH, Nordstjernevej 41, 2600, Glostrup, Denmark
- Department of Clinical Medicine, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark
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8
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Zhu T, Wang Z, Wu W, Ling Y, Wang Z, Zhou C, Fang X, Huang C, Xie C, Chen J, Zhang X. Altered brain functional networks in schizophrenia with persistent negative symptoms: an activation likelihood estimation meta-analysis. Front Hum Neurosci 2023; 17:1204632. [PMID: 37954938 PMCID: PMC10637389 DOI: 10.3389/fnhum.2023.1204632] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/12/2023] [Accepted: 10/10/2023] [Indexed: 11/14/2023] Open
Abstract
Objective To investigate brain structural and functional characteristics of three brain functional networks including default mode network (DMN), central executive network (CEN), and salience network (SN) in persistent negative symptoms (PNS) patients. Methods We performed an activation likelihood estimation (ALE) meta-analysis of functional connectivity (FC) studies and voxel-based morphometry (VBM) studies to detect specific structural and functional alterations of brain networks between PNS patients and healthy controls. Results Seventeen VBM studies and twenty FC studies were included. In the DMN, PNS patients showed decreased gray matter in the bilateral medial frontal gyrus and left anterior cingulate gyrus and a significant reduction of FC in the right precuneus. Also, PNS patients had a decrease of gray matter in the left inferior parietal lobules and medial frontal gyrus, and a significant reduction of FC in the bilateral superior frontal gyrus in the CEN. In comparison with healthy controls, PNS patients exhibited reduced gray matter in the bilateral insula, anterior cingulate gyrus, left precentral gyrus and right claustrum and lower FC in these brain areas in the SN, including the left insula, claustrum, inferior frontal gyrus and extra-nuclear. Conclusion This meta-analysis reveals brain structural and functional imaging alterations in the three networks and the interaction among these networks in PNS patients, which provides neuroscientific evidence for more personalized treatment.Systematic Review RegistrationThe PROSPERO (https://www.crd.york.ac.uk/PROSPERO/, registration number: CRD42022335962).
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Affiliation(s)
- Tingting Zhu
- Department of Psychiatry, The Third People’s Hospital of Huai’an, Huaian, Jiangsu, China
- Department of Geriatric Psychiatry, The Affiliated Brain Hospital of Nanjing Medical University, Nanjing, China
| | - Zengxiu Wang
- Department of Hepatology, The Second Hospital of Nanjing, Nanjing University of Chinese Medicine, Nanjing, China
| | - Weifeng Wu
- Department of Hepatology, The Second Hospital of Nanjing, Nanjing University of Chinese Medicine, Nanjing, China
| | - Yuru Ling
- Department of Geriatric Psychiatry, The Affiliated Brain Hospital of Nanjing Medical University, Nanjing, China
| | - Zixu Wang
- Department of Geriatric Psychiatry, The Affiliated Brain Hospital of Nanjing Medical University, Nanjing, China
| | - Chao Zhou
- Department of Geriatric Psychiatry, The Affiliated Brain Hospital of Nanjing Medical University, Nanjing, China
| | - Xinyu Fang
- Department of Geriatric Psychiatry, The Affiliated Brain Hospital of Nanjing Medical University, Nanjing, China
| | - Chengbing Huang
- Department of Psychiatry, The Third People’s Hospital of Huai’an, Huaian, Jiangsu, China
- Department of Geriatric Psychiatry, The Affiliated Brain Hospital of Nanjing Medical University, Nanjing, China
| | - Chunming Xie
- Department of Neurology, Affiliated Zhongda Hospital, School of Medicine Southeast University, Nanjing, China
| | - Jiu Chen
- Institute of Neuropsychiatry, The Affiliated Brain Hospital of Nanjing Medical University, Nanjing, China
| | - Xiangrong Zhang
- Department of Geriatric Psychiatry, The Affiliated Brain Hospital of Nanjing Medical University, Nanjing, China
- The Affiliated Xuzhou Oriental Hospital of Xuzhou Medical University, Xuzhou, Jiangsu, China
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9
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Chai C, Ding H, Du X, Xie Y, Man W, Zhang Y, Ji Y, Liang M, Zhang B, Ning Y, Zhuo C, Yu C, Qin W. Dissociation between neuroanatomical and symptomatic subtypes in schizophrenia. Eur Psychiatry 2023; 66:e78. [PMID: 37702075 PMCID: PMC10594537 DOI: 10.1192/j.eurpsy.2023.2446] [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: 10/31/2022] [Revised: 05/20/2023] [Accepted: 08/01/2023] [Indexed: 09/14/2023] Open
Abstract
BACKGROUND Schizophrenia is a complex and heterogeneous syndrome with high clinical and biological stratification. Identifying distinctive subtypes can improve diagnostic accuracy and help precise therapy. A key challenge for schizophrenia subtyping is understanding the subtype-specific biological underpinnings of clinical heterogeneity. This study aimed to investigate if the machine learning (ML)-based neuroanatomical and symptomatic subtypes of schizophrenia are associated. METHODS A total of 314 schizophrenia patients and 257 healthy controls from four sites were recruited. Gray matter volume (GMV) and Positive and Negative Syndrome Scale (PANSS) scores were employed to recognize schizophrenia neuroanatomical and symptomatic subtypes using K-means and hierarchical methods, respectively. RESULTS Patients with ML-based neuroanatomical subtype-1 had focally increased GMV, and subtype-2 had widespread reduced GMV than the healthy controls based on either K-means or Hierarchical methods. In contrast, patients with symptomatic subtype-1 had severe PANSS scores than subtype-2. No differences in PANSS scores were shown between the two neuroanatomical subtypes; similarly, no GMV differences were found between the two symptomatic subtypes. Cohen's Kappa test further demonstrated an apparent dissociation between the ML-based neuroanatomical and symptomatic subtypes (P > 0.05). The dissociation patterns were validated in four independent sites with diverse disease progressions (chronic vs. first episodes) and ancestors (Chinese vs. Western). CONCLUSIONS These findings revealed a replicable dissociation between ML-based neuroanatomical and symptomatic subtypes of schizophrenia, which provides a new viewpoint toward understanding the heterogeneity of schizophrenia.
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Affiliation(s)
- Chao Chai
- Department of Radiology and Tianjin Key Laboratory of Functional Imaging, Tianjin Medical University General Hospital, Tianjin, China
| | - Hao Ding
- School of Medical Imaging, Tianjin Medical University, Tianjin, China
| | - Xiaotong Du
- Department of Radiology and Tianjin Key Laboratory of Functional Imaging, Tianjin Medical University General Hospital, Tianjin, China
| | - Yingying Xie
- Department of Radiology and Tianjin Key Laboratory of Functional Imaging, Tianjin Medical University General Hospital, Tianjin, China
| | - Weiqi Man
- Department of Radiology and Tianjin Key Laboratory of Functional Imaging, Tianjin Medical University General Hospital, Tianjin, China
- Academy of Medical Engineering and Translational Medicine, Tianjin University, Tianjin, China
| | - Yu Zhang
- Department of Radiology and Tianjin Key Laboratory of Functional Imaging, Tianjin Medical University General Hospital, Tianjin, China
| | - Yi Ji
- Department of Radiology and Tianjin Key Laboratory of Functional Imaging, Tianjin Medical University General Hospital, Tianjin, China
| | - Meng Liang
- School of Medical Imaging, Tianjin Medical University, Tianjin, China
| | - Bin Zhang
- Department of Psychiatry, Affiliated Brain Hospital of Guangzhou Medical University, Guangzhou, China
| | - Yuping Ning
- Department of Psychiatry, Affiliated Brain Hospital of Guangzhou Medical University, Guangzhou, China
| | - Chuanjun Zhuo
- Department of Psychiatry, Tianjin Fourth Center Hospital, Tianjin, China
| | - Chunshui Yu
- Department of Radiology and Tianjin Key Laboratory of Functional Imaging, Tianjin Medical University General Hospital, Tianjin, China
- School of Medical Imaging, Tianjin Medical University, Tianjin, China
| | - Wen Qin
- Department of Radiology and Tianjin Key Laboratory of Functional Imaging, Tianjin Medical University General Hospital, Tianjin, China
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10
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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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11
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Sreeraj VS, Shivakumar V, Bhalerao GV, Kalmady SV, Narayanaswamy JC, Venkatasubramanian G. Resting-state functional connectivity correlates of antipsychotic treatment in unmedicated schizophrenia. Asian J Psychiatr 2023; 82:103459. [PMID: 36682158 DOI: 10.1016/j.ajp.2023.103459] [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: 10/16/2022] [Revised: 01/03/2023] [Accepted: 01/07/2023] [Indexed: 01/09/2023]
Abstract
BACKGROUND Antipsychotics may modulate the resting state functional connectivity(rsFC) to improve clinical symptoms in schizophrenia(Sz). Existing literature has potential confounders like past medication effects and evaluating preselected regions/networks. We aimed to evaluate connectivity pattern changes with antipsychotics in unmedicated Sz using Multivariate pattern analysis(MVPA), a data-driven technique for whole-brain connectome analysis. METHODS Forty-seven unmedicated patients with Sz(DSM-IV-TR) underwent clinical evaluation and neuroimaging at baseline and after 3-months of antipsychotic treatment. Resting-state functional MRI was analysed using group-MVPA to derive 5-components. The brain region with significant connectivity pattern changes with antipsychotics was identified, and post-hoc seed-to-voxel analysis was performed to identify connectivity changes and their association with symptom changes. RESULTS Connectome-MVPA analysis revealed the connectivity pattern of a cluster localised to left anterior cingulate and paracingulate gyri (ACC/PCG) (peak coordinates:x = -04,y = +30,z = +26;k = 12;cluster-pFWE=0.002) to differ significantly after antipsychotics. Specifically, its connections with clusters of precuneus/posterior cingulate cortex(PCC) and left inferior temporal gyrus(ITG) correlated with improvement in positive and negative symptoms scores, respectively. CONCLUSION ACC/PCG, a hub of the default mode network, seems to mediate the antipsychotic effects in unmedicated Sz. Evaluating causality models with data from randomised controlled design using the MVPA approach would further enhance our understanding of therapeutic connectomics in Sz.
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Affiliation(s)
- Vanteemar S Sreeraj
- InSTAR Clinic and Translational Psychiatry Lab, Department of Psychiatry, National Institute of Mental Health and Neurosciences, Bengaluru, India.
| | - Venkataram Shivakumar
- InSTAR Clinic and Translational Psychiatry Lab, Department of Psychiatry, National Institute of Mental Health and Neurosciences, Bengaluru, India; Department of Integrative Medicine, National Institute of Mental Health and Neurosciences, Bengaluru, India
| | | | - Sunil V Kalmady
- Alberta Machine Intelligence Institute, Department of Computing Science, University of Alberta, Edmonton, Alberta, Canada; Canadian VIGOUR Centre, University of Alberta, Edmonton, Alberta, Canada
| | | | - Ganesan Venkatasubramanian
- InSTAR Clinic and Translational Psychiatry Lab, Department of Psychiatry, National Institute of Mental Health and Neurosciences, Bengaluru, India
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12
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Wei X, Dong S, Su Z, Tang L, Zhao P, Pan C, Wang F, Tang Y, Zhang W, Zhang X. NetMoST: A network-based machine learning approach for subtyping schizophrenia using polygenic SNP allele biomarkers. ARXIV 2023:arXiv:2302.00104v2. [PMID: 36776814 PMCID: PMC9915719] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Subscribe] [Scholar Register] [Indexed: 02/14/2023]
Abstract
Subtyping neuropsychiatric disorders like schizophrenia is essential for improving the diagnosis and treatment of complex diseases. Subtyping schizophrenia is challenging because it is polygenic and genetically heterogeneous, rendering the standard symptom-based diagnosis often unreliable and unrepeatable. We developed a novel network-based machine-learning approach, netMoST, to subtyping psychiatric disorders. NetMoST identifies polygenic risk SNP-allele modules from genome-wide genotyping data as polygenic haplotype biomarkers (PHBs) for disease subtyping. We applied netMoST to subtype a cohort of schizophrenia subjects into three distinct biotypes with differentiable genetic, neuroimaging and functional characteristics. The PHBs of the first biotype (36.9% of all patients) were related to neurodevelopment and cognition, the PHBs of the second biotype (28.4%) were enriched for neuroimmune functions, and the PHBs of the third biotype (34.7%) were associated with the transport of calcium ions and neurotransmitters. Neuroimaging patterns provided additional support to the new biotypes, with unique regional homogeneity (ReHo) patterns observed in the brains of each biotype compared with healthy controls. Our findings demonstrated netMoST's capability for uncovering novel biotypes of complex diseases such as schizophrenia. The results also showed the power of exploring polygenic allelic patterns that transcend the conventional GWAS approaches.
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Affiliation(s)
- Xinru Wei
- Early Intervention Unit, Department of Psychiatry, The Affiliated Brain Hospital of Nanjing Medical University, Nanjing, Jiangsu 210029, China
- School of Biomedical Engineering and Informatics, Nanjing Medical University, Nanjing, Jiangsu 210001, China
| | - Shuai Dong
- School of Biomedical Engineering and Informatics, Nanjing Medical University, Nanjing, Jiangsu 210001, China
| | - Zhao Su
- School of Biomedical Engineering and Informatics, Nanjing Medical University, Nanjing, Jiangsu 210001, China
| | - Lili Tang
- Early Intervention Unit, Department of Psychiatry, The Affiliated Brain Hospital of Nanjing Medical University, Nanjing, Jiangsu 210029, China
- Functional Brain Imaging Institute of Nanjing Medical University, Nanjing, China
| | - Pengfei Zhao
- Early Intervention Unit, Department of Psychiatry, The Affiliated Brain Hospital of Nanjing Medical University, Nanjing, Jiangsu 210029, China
- Functional Brain Imaging Institute of Nanjing Medical University, Nanjing, China
| | - Chunyu Pan
- School of Computer Science and Engineering, Northeastern University, Shenyang, China
| | - Fei Wang
- Early Intervention Unit, Department of Psychiatry, The Affiliated Brain Hospital of Nanjing Medical University, Nanjing, Jiangsu 210029, China
- Functional Brain Imaging Institute of Nanjing Medical University, Nanjing, China
| | - Yanqing Tang
- Department of Psychiatry, The First Affiliated Hospital of China Medical University, Shenyang, China
- Brain Function Research Section, The First Affiliated Hospital of China Medical University, Shenyang, China
- Department of Gerontology, The First Affiliated Hospital of China Medical University, Shenyang, China
| | - Weixiong Zhang
- Department of Health Technology and Informatics, Department of Computing, The Hong Kong Polytechnic University, Hong Kong, China
| | - Xizhe Zhang
- School of Biomedical Engineering and Informatics, Nanjing Medical University, Nanjing, Jiangsu 210001, China
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13
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Gao Z, Xiao Y, Zhu F, Tao B, Yu W, Lui S. The whole-brain connectome landscape in patients with schizophrenia: a systematic review and meta-analysis of graph theoretical characteristics. Neurosci Biobehav Rev 2023; 148:105144. [PMID: 36990373 DOI: 10.1016/j.neubiorev.2023.105144] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/01/2022] [Revised: 03/14/2023] [Accepted: 03/24/2023] [Indexed: 03/29/2023]
Abstract
The alterations of connectome in schizophrenia have been reported, but the results remain inconsistent. We conducted a systematic review and random-effects meta-analysis on structural or functional connectome MRI studies comparing global graph theoretical characteristics between schizophrenia and healthy controls. Meta-regression and subgroup analyses were performed to examine confounding effects. Based on the included 48 studies, Structural connectome in schizophrenia showed a significant decrease in segregation (lower clustering coefficient and local efficiency, Hedge's g= -0.352 and -0.864, respectively) and integration (higher characteristic path length and lower global efficiency, Hedge's g= 0.532 and -0.577 respectively). The functional connectome showed no difference between groups except γ. Moderator analysis indicated that clinical and methodological factors exerted a potential effect on the graph theoretical characteristics. Our analysis revealed a weaker small-worldization trend in structural connectome of schizophrenia. For the relatively unchanged functional connectome, more homogenous and high-quality studies are warranted to elucidate whether the change was blurred by heterogeneity or the presentation of pathophysiological reconfiguration.
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14
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Ji GJ, Zalesky A, Wang Y, He K, Wang L, Du R, Sun J, Bai T, Chen X, Tian Y, Zhu C, Wang K. Linking Personalized Brain Atrophy to Schizophrenia Network and Treatment Response. Schizophr Bull 2023; 49:43-52. [PMID: 36318234 PMCID: PMC9810021 DOI: 10.1093/schbul/sbac162] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 11/07/2022]
Abstract
BACKGROUND AND HYPOTHESIS Schizophrenia manifests with marked heterogeneity in both clinical presentation and underlying biology. Modeling individual differences within clinical cohorts is critical to translate knowledge reliably into clinical practice. We hypothesized that individualized brain atrophy in patients with schizophrenia may explain the heterogeneous outcomes of repetitive transcranial magnetic stimulation (rTMS). STUDY DESIGN The magnetic resonance imaging (MRI) data of 797 healthy subjects and 91 schizophrenia patients (between January 1, 2015, and December 31, 2020) were retrospectively selected from our hospital database. The healthy subjects were used to establish normative reference ranges for cortical thickness as a function of age and sex. Then, a schizophrenia patient's personalized atrophy map was computed as vertex-wise deviations from the normative model. Each patient's atrophy network was mapped using resting-state functional connectivity MRI from a subgroup of healthy subjects (n = 652). In total 52 of the 91 schizophrenia patients received rTMS in a randomized clinical trial (RCT). Their longitudinal symptom changes were adopted to test the clinical utility of the personalized atrophy map. RESULTS The personalized atrophy maps were highly heterogeneous across patients, but functionally converged to a putative schizophrenia network that comprised regions implicated by previous group-level findings. More importantly, retrospective analysis of rTMS-RCT data indicated that functional connectivity of the personalized atrophy maps with rTMS targets was significantly associated with the symptom outcomes of schizophrenia patients. CONCLUSIONS Normative modeling can aid in mapping the personalized atrophy network associated with treatment outcomes of patients with schizophrenia.
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Affiliation(s)
- Gong-Jun Ji
- Department of Neurology, The First Affiliated Hospital of Anhui Medical University, The School of Mental Health and Psychological Sciences, Anhui Medical University, Hefei, 230032, China
- Institute of Artificial Intelligence, Hefei Comprehensive National Science Center, Hefei, 230088, China
- Anhui Province Key Laboratory of Cognition and Neuropsychiatric Disorders, Hefei, 230032, China
- Collaborative Innovation Center of Neuropsychiatric Disorders and Mental Health, Anhui Province, 230032, China
- Anhui Institute of Translational Medicine, Hefei, 230032, China
| | - Andrew Zalesky
- Departments of Psychiatry and Biomedical Engineering, Melbourne Neuropsychiatry Centre, The University of Melbourne, Victoria, 3010, Australia
| | - Yingru Wang
- Department of Neurology, The First Affiliated Hospital of Anhui Medical University, The School of Mental Health and Psychological Sciences, Anhui Medical University, Hefei, 230032, China
- Institute of Artificial Intelligence, Hefei Comprehensive National Science Center, Hefei, 230088, China
- Anhui Province Key Laboratory of Cognition and Neuropsychiatric Disorders, Hefei, 230032, China
- Collaborative Innovation Center of Neuropsychiatric Disorders and Mental Health, Anhui Province, 230032, China
| | - Kongliang He
- Anhui Province Key Laboratory of Cognition and Neuropsychiatric Disorders, Hefei, 230032, China
- Collaborative Innovation Center of Neuropsychiatric Disorders and Mental Health, Anhui Province, 230032, China
- Anhui Institute of Translational Medicine, Hefei, 230032, China
- Department of Psychiatry, Anhui Mental Health Center, Hefei, 230022, China
| | - Lu Wang
- Department of Neurology, The First Affiliated Hospital of Anhui Medical University, The School of Mental Health and Psychological Sciences, Anhui Medical University, Hefei, 230032, China
- Institute of Artificial Intelligence, Hefei Comprehensive National Science Center, Hefei, 230088, China
- Anhui Province Key Laboratory of Cognition and Neuropsychiatric Disorders, Hefei, 230032, China
- Collaborative Innovation Center of Neuropsychiatric Disorders and Mental Health, Anhui Province, 230032, China
| | - Rongrong Du
- Department of Neurology, The First Affiliated Hospital of Anhui Medical University, The School of Mental Health and Psychological Sciences, Anhui Medical University, Hefei, 230032, China
- Institute of Artificial Intelligence, Hefei Comprehensive National Science Center, Hefei, 230088, China
- Anhui Province Key Laboratory of Cognition and Neuropsychiatric Disorders, Hefei, 230032, China
- Collaborative Innovation Center of Neuropsychiatric Disorders and Mental Health, Anhui Province, 230032, China
| | - Jinmei Sun
- Department of Neurology, The First Affiliated Hospital of Anhui Medical University, The School of Mental Health and Psychological Sciences, Anhui Medical University, Hefei, 230032, China
- Institute of Artificial Intelligence, Hefei Comprehensive National Science Center, Hefei, 230088, China
- Anhui Province Key Laboratory of Cognition and Neuropsychiatric Disorders, Hefei, 230032, China
- Collaborative Innovation Center of Neuropsychiatric Disorders and Mental Health, Anhui Province, 230032, China
| | - Tongjian Bai
- Department of Neurology, The First Affiliated Hospital of Anhui Medical University, The School of Mental Health and Psychological Sciences, Anhui Medical University, Hefei, 230032, China
- Institute of Artificial Intelligence, Hefei Comprehensive National Science Center, Hefei, 230088, China
- Anhui Province Key Laboratory of Cognition and Neuropsychiatric Disorders, Hefei, 230032, China
- Collaborative Innovation Center of Neuropsychiatric Disorders and Mental Health, Anhui Province, 230032, China
| | - Xingui Chen
- Department of Neurology, The First Affiliated Hospital of Anhui Medical University, The School of Mental Health and Psychological Sciences, Anhui Medical University, Hefei, 230032, China
- Institute of Artificial Intelligence, Hefei Comprehensive National Science Center, Hefei, 230088, China
- Anhui Province Key Laboratory of Cognition and Neuropsychiatric Disorders, Hefei, 230032, China
- Collaborative Innovation Center of Neuropsychiatric Disorders and Mental Health, Anhui Province, 230032, China
| | - Yanghua Tian
- Department of Neurology, The First Affiliated Hospital of Anhui Medical University, The School of Mental Health and Psychological Sciences, Anhui Medical University, Hefei, 230032, China
- Institute of Artificial Intelligence, Hefei Comprehensive National Science Center, Hefei, 230088, China
- Anhui Province Key Laboratory of Cognition and Neuropsychiatric Disorders, Hefei, 230032, China
- Collaborative Innovation Center of Neuropsychiatric Disorders and Mental Health, Anhui Province, 230032, China
- Anhui Institute of Translational Medicine, Hefei, 230032, China
| | - Chunyan Zhu
- Department of Neurology, The First Affiliated Hospital of Anhui Medical University, The School of Mental Health and Psychological Sciences, Anhui Medical University, Hefei, 230032, China
- Institute of Artificial Intelligence, Hefei Comprehensive National Science Center, Hefei, 230088, China
- Anhui Province Key Laboratory of Cognition and Neuropsychiatric Disorders, Hefei, 230032, China
- Collaborative Innovation Center of Neuropsychiatric Disorders and Mental Health, Anhui Province, 230032, China
- Anhui Institute of Translational Medicine, Hefei, 230032, China
| | - Kai Wang
- Department of Neurology, The First Affiliated Hospital of Anhui Medical University, The School of Mental Health and Psychological Sciences, Anhui Medical University, Hefei, 230032, China
- Institute of Artificial Intelligence, Hefei Comprehensive National Science Center, Hefei, 230088, China
- Anhui Province Key Laboratory of Cognition and Neuropsychiatric Disorders, Hefei, 230032, China
- Collaborative Innovation Center of Neuropsychiatric Disorders and Mental Health, Anhui Province, 230032, China
- Anhui Institute of Translational Medicine, Hefei, 230032, China
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15
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Luo L, You W, DelBello MP, Gong Q, Li F. Recent advances in psychoradiology. Phys Med Biol 2022; 67. [DOI: 10.1088/1361-6560/ac9d1e] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/04/2022] [Accepted: 10/24/2022] [Indexed: 11/24/2022]
Abstract
Abstract
Psychiatry, as a field, lacks objective markers for diagnosis, progression, treatment planning, and prognosis, in part due to difficulties studying the brain in vivo, and diagnoses are based on self-reported symptoms and observation of patient behavior and cognition. Rapid advances in brain imaging techniques allow clinical investigators to noninvasively quantify brain features at the structural, functional, and molecular levels. Psychoradiology is an emerging discipline at the intersection of psychiatry and radiology. Psychoradiology applies medical imaging technologies to psychiatry and promises not only to improve insight into structural and functional brain abnormalities in patients with psychiatric disorders but also to have potential clinical utility. We searched for representative studies related to recent advances in psychoradiology through May 1, 2022, and conducted a selective review of 165 references, including 75 research articles. We summarize the novel dynamic imaging processing methods to model brain networks and present imaging genetics studies that reveal the relationship between various neuroimaging endophenotypes and genetic markers in psychiatric disorders. Furthermore, we survey recent advances in psychoradiology, with a focus on future psychiatric diagnostic approaches with dimensional analysis and a shift from group-level to individualized analysis. Finally, we examine the application of machine learning in psychoradiology studies and the potential of a novel option for brain stimulation treatment based on psychoradiological findings in precision medicine. Here, we provide a summary of recent advances in psychoradiology research, and we hope this review will help guide the practice of psychoradiology in the scientific and clinical fields.
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16
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Li Q, Yao L, You W, Liu J, Deng S, Li B, Luo L, Zhao Y, Wang Y, Wang Y, Zhang Q, Long F, Sweeney JA, Gu S, Li F, Gong Q. Controllability of Functional Brain Networks and Its Clinical Significance in First-Episode Schizophrenia. Schizophr Bull 2022; 49:659-668. [PMID: 36402458 PMCID: PMC10154712 DOI: 10.1093/schbul/sbac177] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 11/21/2022]
Abstract
BACKGROUND AND HYPOTHESIS Disrupted control of brain state transitions may contribute to the diverse dysfunctions of cognition, emotion, and behavior that are fundamental to schizophrenia. Control theory provides the rationale for evaluating brain state transitions from a controllability perspective, which may help reveal the brain mechanism for clinical features such as cognitive control deficits associated with schizophrenia. We hypothesized that brain controllability would be altered in patients with schizophrenia, and that controllability of brain networks would be related to clinical symptomatology. STUDY DESIGN Controllability measurements of functional brain networks, including average controllability and modal controllability, were calculated and compared between 125 first-episode never-treated patients with schizophrenia and 133 healthy controls (HCs). Associations between controllability metrics and clinical symptoms were evaluated using sparse canonical correlation analysis. STUDY RESULTS Compared to HCs, patients showed significantly increased average controllability (PFDR = .023) and decreased modal controllability (PFDR = .023) in dorsal anterior cingulate cortex (dACC). General psychopathology symptoms and positive symptoms were positively correlated with average controllability in regions of default mode network and negatively associated with average controllability in regions of sensorimotor, dorsal attention, and frontoparietal networks. CONCLUSIONS Our findings suggest that altered controllability of functional activity in dACC may play a critical role in the pathophysiology of schizophrenia, consistent with the importance of this region in cognitive and brain state control operations. The demonstration of associations of functional controllability with psychosis symptoms suggests that the identified alterations in average controllability of brain function may contribute to the severity of acute psychotic illness in schizophrenia.
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Affiliation(s)
- Qian Li
- Huaxi MR Research Center (HMRRC), Department of Radiology, West China Hospital of Sichuan University, Chengdu 610041, Sichuan, P.R. China.,Research Unit of Psychoradiology, Chinese Academy of Medical Sciences, Chengdu 610041, Sichuan, P.R. China.,Functional and Molecular Imaging Key Laboratory, Sichuan University, Chengdu 610041, Sichuan, P.R. China
| | - Li Yao
- Huaxi MR Research Center (HMRRC), Department of Radiology, West China Hospital of Sichuan University, Chengdu 610041, Sichuan, P.R. China.,Research Unit of Psychoradiology, Chinese Academy of Medical Sciences, Chengdu 610041, Sichuan, P.R. China.,Functional and Molecular Imaging Key Laboratory, Sichuan University, Chengdu 610041, Sichuan, P.R. China
| | - Wanfang You
- Huaxi MR Research Center (HMRRC), Department of Radiology, West China Hospital of Sichuan University, Chengdu 610041, Sichuan, P.R. China.,Research Unit of Psychoradiology, Chinese Academy of Medical Sciences, Chengdu 610041, Sichuan, P.R. China.,Functional and Molecular Imaging Key Laboratory, Sichuan University, Chengdu 610041, Sichuan, P.R. China
| | - Jiang Liu
- Department of Computer Science and Engineering, University of Electronic Science and Technology of China, Chengdu, China
| | - Shikuang Deng
- Department of Computer Science and Engineering, University of Electronic Science and Technology of China, Chengdu, China
| | - Bin Li
- Department of Computer Science and Engineering, University of Electronic Science and Technology of China, Chengdu, China
| | - Lekai Luo
- Huaxi MR Research Center (HMRRC), Department of Radiology, West China Hospital of Sichuan University, Chengdu 610041, Sichuan, P.R. China.,Research Unit of Psychoradiology, Chinese Academy of Medical Sciences, Chengdu 610041, Sichuan, P.R. China.,Functional and Molecular Imaging Key Laboratory, Sichuan University, Chengdu 610041, Sichuan, P.R. China
| | - Youjin Zhao
- Huaxi MR Research Center (HMRRC), Department of Radiology, West China Hospital of Sichuan University, Chengdu 610041, Sichuan, P.R. China.,Research Unit of Psychoradiology, Chinese Academy of Medical Sciences, Chengdu 610041, Sichuan, P.R. China.,Functional and Molecular Imaging Key Laboratory, Sichuan University, Chengdu 610041, Sichuan, P.R. China
| | - Yuxia Wang
- Huaxi MR Research Center (HMRRC), Department of Radiology, West China Hospital of Sichuan University, Chengdu 610041, Sichuan, P.R. China.,Research Unit of Psychoradiology, Chinese Academy of Medical Sciences, Chengdu 610041, Sichuan, P.R. China.,Functional and Molecular Imaging Key Laboratory, Sichuan University, Chengdu 610041, Sichuan, P.R. China
| | - Yaxuan Wang
- Huaxi MR Research Center (HMRRC), Department of Radiology, West China Hospital of Sichuan University, Chengdu 610041, Sichuan, P.R. China.,Research Unit of Psychoradiology, Chinese Academy of Medical Sciences, Chengdu 610041, Sichuan, P.R. China.,Functional and Molecular Imaging Key Laboratory, Sichuan University, Chengdu 610041, Sichuan, P.R. China
| | - Qian Zhang
- Huaxi MR Research Center (HMRRC), Department of Radiology, West China Hospital of Sichuan University, Chengdu 610041, Sichuan, P.R. China.,Research Unit of Psychoradiology, Chinese Academy of Medical Sciences, Chengdu 610041, Sichuan, P.R. China.,Functional and Molecular Imaging Key Laboratory, Sichuan University, Chengdu 610041, Sichuan, P.R. China
| | - Fenghua Long
- Huaxi MR Research Center (HMRRC), Department of Radiology, West China Hospital of Sichuan University, Chengdu 610041, Sichuan, P.R. China.,Research Unit of Psychoradiology, Chinese Academy of Medical Sciences, Chengdu 610041, Sichuan, P.R. China.,Functional and Molecular Imaging Key Laboratory, Sichuan University, Chengdu 610041, Sichuan, P.R. China
| | - John A Sweeney
- Huaxi MR Research Center (HMRRC), Department of Radiology, West China Hospital of Sichuan University, Chengdu 610041, Sichuan, P.R. China.,Department of Psychiatry and Behavioral Neuroscience, University of Cincinnati, Cincinnati, OH 45219, USA
| | - Shi Gu
- Department of Computer Science and Engineering, University of Electronic Science and Technology of China, Chengdu, China
| | - Fei Li
- Huaxi MR Research Center (HMRRC), Department of Radiology, West China Hospital of Sichuan University, Chengdu 610041, Sichuan, P.R. China.,Research Unit of Psychoradiology, Chinese Academy of Medical Sciences, Chengdu 610041, Sichuan, P.R. China.,Functional and Molecular Imaging Key Laboratory, Sichuan University, Chengdu 610041, Sichuan, P.R. China
| | - Qiyong Gong
- Huaxi MR Research Center (HMRRC), Department of Radiology, West China Hospital of Sichuan University, Chengdu 610041, Sichuan, P.R. China.,Research Unit of Psychoradiology, Chinese Academy of Medical Sciences, Chengdu 610041, Sichuan, P.R. China.,Functional and Molecular Imaging Key Laboratory, Sichuan University, Chengdu 610041, Sichuan, P.R. China
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17
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Impaired dynamic functional brain properties and their relationship to symptoms in never treated first-episode patients with schizophrenia. SCHIZOPHRENIA (HEIDELBERG, GERMANY) 2022; 8:90. [PMID: 36309537 PMCID: PMC9617869 DOI: 10.1038/s41537-022-00299-9] [Citation(s) in RCA: 9] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/25/2022] [Accepted: 10/14/2022] [Indexed: 11/07/2022]
Abstract
Studies of dynamic functional connectivity (dFC) and topology can provide novel insights into the neurophysiology of brain dysfunction in schizophrenia and its relation to core symptoms of psychosis. Limited investigations of these disturbances have been conducted with never-treated first-episode patients to avoid the confounds of treatment or chronic illness. Therefore, we recruited 95 acutely ill, first-episode, never-treated patients with schizophrenia and examined brain dFC patterns relative to healthy controls using resting-state functional magnetic resonance imaging and a sliding-window approach. We compared the dynamic attributes at the group level and found patients spent more time in a hypoconnected state and correspondingly less time in a hyperconnected state. Patients demonstrated decreased dynamics of nodal efficiency and eigenvector centrality (EC) in the right medial prefrontal cortex, which was associated with psychosis severity reflected in Positive and Negative Syndrome Scale ratings. We also observed increased dynamics of EC in temporal and sensorimotor regions. These findings were supported by validation analysis. To supplement the group comparison analyses, a support vector classifier was used to identify the dynamic attributes that best distinguished patients from controls at the individual level. Selected features for case-control classification were highly coincident with the properties having significant between-group differences. Our findings provide novel neuroimaging evidence about dynamic characteristics of brain physiology in acute schizophrenia. The clinically relevant atypical pattern of dynamic shifting between brain states in schizophrenia may represent a critical aspect of illness pathophysiology underpinning its defining cognitive, behavioral, and affective features.
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18
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Menon V, Palaniyappan L, Supekar K. Integrative Brain Network and Salience Models of Psychopathology and Cognitive Dysfunction in Schizophrenia. Biol Psychiatry 2022:S0006-3223(22)01637-7. [PMID: 36702660 DOI: 10.1016/j.biopsych.2022.09.029] [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: 03/25/2022] [Revised: 08/09/2022] [Accepted: 09/06/2022] [Indexed: 01/28/2023]
Abstract
Brain network models of cognitive control are central to advancing our understanding of psychopathology and cognitive dysfunction in schizophrenia. This review examines the role of large-scale brain organization in schizophrenia, with a particular focus on a triple-network model of cognitive control and its role in aberrant salience processing. First, we provide an overview of the triple network involving the salience, frontoparietal, and default mode networks and highlight the central role of the insula-anchored salience network in the aberrant mapping of salient external and internal events in schizophrenia. We summarize the extensive evidence that has emerged from structural, neurochemical, and functional brain imaging studies for aberrancies in these networks and their dynamic temporal interactions in schizophrenia. Next, we consider the hypothesis that atypical striatal dopamine release results in misattribution of salience to irrelevant external stimuli and self-referential mental events. We propose an integrated triple-network salience-based model incorporating striatal dysfunction and sensitivity to perceptual and cognitive prediction errors in the insula node of the salience network and postulate that dysregulated dopamine modulation of salience network-centered processes contributes to the core clinical phenotype of schizophrenia. Thus, a powerful paradigm to characterize the neurobiology of schizophrenia emerges when we combine conceptual models of salience with large-scale cognitive control networks in a unified manner. We conclude by discussing potential therapeutic leads on restoring brain network dysfunction in schizophrenia.
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Affiliation(s)
- Vinod Menon
- Department of Psychiatry and Behavioral Sciences, Stanford University School of Medicine, Stanford, California; Department of Neurology and Neurological Sciences, Stanford University School of Medicine, Stanford, California; Wu Tsai Neurosciences Institute, Stanford University School of Medicine, Stanford, California.
| | - Lena Palaniyappan
- Department of Psychiatry and Robarts Research Institute, University of Western Ontario, London, Ontario, Canada; Lawson Health Research Institute, London, Ontario, Canada; Douglas Mental Health University Institute, Montreal, Quebec, Canada; Department of Psychiatry, McGill University, Montreal, Quebec, Canada
| | - Kaustubh Supekar
- Department of Psychiatry and Behavioral Sciences, Stanford University School of Medicine, Stanford, California; Wu Tsai Neurosciences Institute, Stanford University School of Medicine, Stanford, California
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19
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Neurometabolic and functional changes of default-mode network relate to clinical recovery in first-episode psychosis patients: A longitudinal 1H-MRS and fMRI study. Neuroimage Clin 2022; 34:102970. [PMID: 35240468 PMCID: PMC8889416 DOI: 10.1016/j.nicl.2022.102970] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/18/2021] [Revised: 02/12/2022] [Accepted: 02/21/2022] [Indexed: 02/06/2023]
Abstract
BACKGROUND Antipsychotic treatment has improved the disrupted functional connectivity (FC) and neurometabolites levels of the default mode network (DMN) in schizophrenia patients, but a direct relationship between FC change, neurometabolic level alteration, and symptom improvement has not been built. This study examined the association between the alterations in DMN FC, the changes of neurometabolites levels in the medial prefrontal cortex (MPFC), and the improvementsinpsychopathology in a longitudinal study of drug-naïve first-episode psychosis (FEP) patients. METHODS Thirty-two drug-naïve FEP patients and 30 matched healthy controls underwent repeated assessments with the Positive and Negative Syndrome Scale (PANSS) and 3T proton magnetic resonance spectroscopy as well as resting-state functional magnetic resonance imaging. The levels of γ-aminobutyric acid, glutamate, N-acetyl-aspartate in MPFC, and the FC of DMN were measured. After 8-week antipsychotic treatment, 24 patients were re-examined. RESULTS After treatment, the changes in γ-aminobutyric acid were correlated with the alterations of FC between the MPFC and DMN, while the changes in N-acetyl-aspartate were associated with the alterations of FC between the posterior cingulate cortex/precuneus and DMN. The FC changes of both regions were correlated with patients PANSS positive score reductions. The structural equation modeling analyses revealed that the changes of DMN FC mediated the relationship between the changes of neurometabolites and the symptom improvements of the patients. CONCLUSIONS The derived neurometabolic-functional changes underlying the clinical recovery provide insights into the prognosis of FEP patients. It is noteworthy that this is an exploratory study, and future work with larger sample size is needed to validate our findings.
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20
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Insula volumes in first-episode and chronic psychosis: A longitudinal MRI study. Schizophr Res 2022; 241:14-23. [PMID: 35074528 DOI: 10.1016/j.schres.2021.12.048] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/01/2021] [Revised: 07/21/2021] [Accepted: 12/28/2021] [Indexed: 12/22/2022]
Abstract
BACKGROUND Alterations in insular grey matter (GM) volume has been consistently reported for affective and non-affective psychoses both in chronic and first-episode patients, ultimately suggesting that the insula might represent a good region to study in order to assess the longitudinal course of psychotic disorders. Therefore, in this longitudinal Magnetic Resonance Imaging (MRI) study, we aimed at further investigating the key role of insular volumes in psychosis. MATERIAL AND METHODS 68 First-Episode Psychosis (FEP) patients, 68 patients with Schizophrenia (SCZ), 47 Bipolar Disorder (BD) patients, and 94 Healthy Controls (HC) were enrolled and underwent a 1.5 T MRI evaluation. A subsample of 99 subjects (10 HC, 23 BD, 29 SCZ, 37 FEP) was rescanned after 2,53 ± 1,68 years. The insular cortex was manually traced and then divided into an anterior and posterior portion. Group and correlation analyses were then performed both at baseline and at follow-up. RESULTS At baseline, greater anterior and lower posterior insular GM volumes were observed in chronic patients. At follow-up, we found that FEP patients had a significant GM volume increase from baseline to follow-up, especially in the posterior insula whereas chronic patients showed a relative stability. Finally, significant negative correlations between illness severity and pharmacological treatment and insular GM volumes were observed in the whole group of psychotic patients. CONCLUSIONS The longitudinal assessment of both chronic and first-episode patients allowed us to detect a complex pattern of GM abnormalities in selective sub-portions of insular volumes, ultimately suggesting that this structure could represent a key biological marker of psychotic disorders.
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21
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Li F, Sun H, Biswal BB, Sweeney JA, Gong Q. Artificial intelligence applications in psychoradiology. PSYCHORADIOLOGY 2021; 1:94-107. [PMID: 37881257 PMCID: PMC10594695 DOI: 10.1093/psyrad/kkab009] [Citation(s) in RCA: 41] [Impact Index Per Article: 13.7] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/30/2021] [Revised: 05/10/2021] [Accepted: 06/04/2021] [Indexed: 02/05/2023]
Abstract
One important challenge in psychiatric research is to translate findings from brain imaging research studies that identified brain alterations in patient groups into an accurate diagnosis at an early stage of illness, prediction of prognosis before treatment, and guidance for selection of effective treatments that target patient-relevant pathophysiological features. This is the primary aim of the field of Psychoradiology. Using databases collected from large samples at multiple centers, sophisticated artificial intelligence (AI) algorithms may be used to develop clinically useful image analysis pipelines that can help physicians diagnose, predict, and make treatment decisions. In this review, we selectively summarize psychoradiological research using magnetic resonance imaging of the brain to explore the neural mechanism of psychiatric disorders, and outline progress and the path forward for the combination of psychoradiology and AI for complementing clinical examinations in patients with psychiatric disorders, as well as limitations in the application of AI that should be considered in future translational research.
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Affiliation(s)
- Fei Li
- Huaxi MR Research Center (HMRRC), Department of Radiology, West China Hospital of Sichuan University, Chengdu 610041, Sichuan, P.R. China
- Research Unit of Psychoradiology, Chinese Academy of Medical Sciences, Chengdu 610041, Sichuan, P.R. China
- Functional and Molecular Imaging Key Laboratory of Sichuan Provience, Department of Radiology, West China Hospital of Sichuan University, Chengdu 610041, Sichuan, P.R. China
| | - Huaiqiang Sun
- Huaxi MR Research Center (HMRRC), Department of Radiology, West China Hospital of Sichuan University, Chengdu 610041, Sichuan, P.R. China
- Research Unit of Psychoradiology, Chinese Academy of Medical Sciences, Chengdu 610041, Sichuan, P.R. China
- Functional and Molecular Imaging Key Laboratory of Sichuan Provience, Department of Radiology, West China Hospital of Sichuan University, Chengdu 610041, Sichuan, P.R. China
| | - Bharat B Biswal
- Department of Biomedical Engineering, New Jersey Institute of Technology, Newark, NJ 07102, USA
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Lab for Neuroinformation, University of Electronic Science and Technology of China, Chengdu 610054, Sichuan, P.R. China
| | - John A Sweeney
- Huaxi MR Research Center (HMRRC), Department of Radiology, West China Hospital of Sichuan University, Chengdu 610041, Sichuan, P.R. China
- Department of Psychiatry and Behavioral Neuroscience, University of Cincinnati, Cincinnati, OH 45219, USA
| | - Qiyong Gong
- Huaxi MR Research Center (HMRRC), Department of Radiology, West China Hospital of Sichuan University, Chengdu 610041, Sichuan, P.R. China
- Research Unit of Psychoradiology, Chinese Academy of Medical Sciences, Chengdu 610041, Sichuan, P.R. China
- Functional and Molecular Imaging Key Laboratory of Sichuan Provience, Department of Radiology, West China Hospital of Sichuan University, Chengdu 610041, Sichuan, P.R. China
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