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Totzek JF, Chakravarty MM, Joober R, Malla A, Shah JL, Raucher-Chéné D, Young AL, Hernaus D, Lepage M, Lavigne KM. Longitudinal inference of multiscale markers in psychosis: from hippocampal centrality to functional outcome. Mol Psychiatry 2024:10.1038/s41380-024-02549-x. [PMID: 38605172 DOI: 10.1038/s41380-024-02549-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/06/2023] [Revised: 03/28/2024] [Accepted: 04/02/2024] [Indexed: 04/13/2024]
Abstract
Multiscale neuroscience conceptualizes mental illness as arising from aberrant interactions across and within multiple biopsychosocial scales. We leverage this framework to propose a multiscale disease progression model of psychosis, in which hippocampal-cortical dysconnectivity precedes impairments in episodic memory and social cognition, which lead to more severe negative symptoms and lower functional outcome. As psychosis represents a heterogeneous collection of biological and behavioral alterations that evolve over time, we further predict this disease progression for a subtype of the patient sample, with other patients showing normal-range performance on all variables. We sampled data from two cross-sectional datasets of first- and multi-episode psychosis, resulting in a sample of 163 patients and 119 non-clinical controls. To address our proposed disease progression model and evaluate potential heterogeneity, we applied a machine-learning algorithm, SuStaIn, to the patient data. SuStaIn uniquely integrates clustering and disease progression modeling and identified three patient subtypes. Subtype 0 showed normal-range performance on all variables. In comparison, Subtype 1 showed lower episodic memory, social cognition, functional outcome, and higher negative symptoms, while Subtype 2 showed lower hippocampal-cortical connectivity and episodic memory. Subtype 1 deteriorated from episodic memory to social cognition, negative symptoms, functional outcome to bilateral hippocampal-cortical dysconnectivity, while Subtype 2 deteriorated from bilateral hippocampal-cortical dysconnectivity to episodic memory and social cognition, functional outcome to negative symptoms. This first application of SuStaIn in a multiscale psychiatric model provides distinct disease trajectories of hippocampal-cortical connectivity, which might underlie the heterogeneous behavioral manifestations of psychosis.
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Affiliation(s)
- Jana F Totzek
- Department of Psychiatry, McGill University, Montreal, QC, Canada
- Douglas Research Centre, Montreal, QC, Canada
- Faculty of Psychology and Neuroscience, Maastricht University, Maastricht, The Netherlands
| | - M Mallar Chakravarty
- Department of Psychiatry, McGill University, Montreal, QC, Canada
- Douglas Research Centre, Montreal, QC, Canada
- Department of Biological and Biomedical Engineering, McGill University, Montreal, QC, Canada
| | - Ridha Joober
- Department of Psychiatry, McGill University, Montreal, QC, Canada
- Douglas Research Centre, Montreal, QC, Canada
| | - Ashok Malla
- Department of Psychiatry, McGill University, Montreal, QC, Canada
- Douglas Research Centre, Montreal, QC, Canada
| | - Jai L Shah
- Department of Psychiatry, McGill University, Montreal, QC, Canada
- Douglas Research Centre, Montreal, QC, Canada
| | - Delphine Raucher-Chéné
- Department of Psychiatry, McGill University, Montreal, QC, Canada
- Douglas Research Centre, Montreal, QC, Canada
| | - Alexandra L Young
- Department of Computer Science, University College London, London, United Kingdom
| | - Dennis Hernaus
- Department of Psychiatry & Neuropsychology, School for Mental Health and NeuroScience MHeNS, Maastricht University, Maastricht, The Netherlands
| | - Martin Lepage
- Department of Psychiatry, McGill University, Montreal, QC, Canada
- Douglas Research Centre, Montreal, QC, Canada
| | - Katie M Lavigne
- Department of Psychiatry, McGill University, Montreal, QC, Canada.
- Douglas Research Centre, Montreal, QC, Canada.
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Dickie EW, Shahab S, Hawco C, Miranda D, Herman G, Argyelan M, Ji JL, Jeyachandra J, Anticevic A, Malhotra AK, Voineskos AN. Robust hierarchically organized whole-brain patterns of dysconnectivity in schizophrenia spectrum disorders observed after personalized intrinsic network topography. Hum Brain Mapp 2023; 44:5153-5166. [PMID: 37605827 PMCID: PMC10502662 DOI: 10.1002/hbm.26453] [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: 01/10/2023] [Revised: 07/05/2023] [Accepted: 08/01/2023] [Indexed: 08/23/2023] Open
Abstract
BACKGROUND Spatial patterns of brain functional connectivity can vary substantially at the individual level. Applying cortical surface-based approaches with individualized rather than group templates may accelerate the discovery of biological markers related to psychiatric disorders. We investigated cortico-subcortical networks from multi-cohort data in people with schizophrenia spectrum disorders (SSDs) and healthy controls (HC) using individualized connectivity profiles. METHODS We utilized resting-state and anatomical MRI data from n = 406 participants (n = 203 SSD, n = 203 HC) from four cohorts. Functional timeseries were extracted from previously defined intrinsic network subregions of the striatum, thalamus, and cerebellum as well as 80 cortical regions of interest, representing six intrinsic networks using (1) volume-based approaches, (2) a surface-based group atlas approaches, and (3) Personalized Intrinsic Network Topography (PINT). RESULTS The correlations between all cortical networks and the expected subregions of the striatum, cerebellum, and thalamus were increased using a surface-based approach (Cohen's D volume vs. surface 0.27-1.00, all p < 10-6 ) and further increased after PINT (Cohen's D surface vs. PINT 0.18-0.96, all p < 10-4 ). In SSD versus HC comparisons, we observed robust patterns of dysconnectivity that were strengthened using a surface-based approach and PINT (Number of differing pairwise-correlations: volume: 404, surface: 570, PINT: 628, FDR corrected). CONCLUSION Surface-based and individualized approaches can more sensitively delineate cortical network dysconnectivity differences in people with SSDs. These robust patterns of dysconnectivity were visibly organized in accordance with the cortical hierarchy, as predicted by computational models.
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Affiliation(s)
- Erin W. Dickie
- Center for Addiction and Mental HealthCampbell Family Mental Health ResearchTorontoOntarioCanada
- Department of PsychiatryUniversity of TorontoTorontoOntarioUSA
| | - Saba Shahab
- Department of MedicineUniversity of OttawaOttawaOntarioCanada
| | - Colin Hawco
- Center for Addiction and Mental HealthCampbell Family Mental Health ResearchTorontoOntarioCanada
- Department of PsychiatryUniversity of TorontoTorontoOntarioUSA
| | - Dayton Miranda
- Center for Addiction and Mental HealthCampbell Family Mental Health ResearchTorontoOntarioCanada
| | - Gabrielle Herman
- Center for Addiction and Mental HealthCampbell Family Mental Health ResearchTorontoOntarioCanada
| | - Miklos Argyelan
- Psychiatry Research, The Zucker Hillside HospitalGlen CoveNew YorkUSA
- Institute of Behavioral Science, Feinstein Institutes for Medical ResearchManhassetNew YorkUSA
- Donald and Barbara Zucker School of Medicine at Hofstra/NorthwellHempsteadNew YorkUSA
| | - Jie Lisa Ji
- Department of PsychiatryYale UniversityNew HavenConnecticutUSA
| | - Jerrold Jeyachandra
- Center for Addiction and Mental HealthCampbell Family Mental Health ResearchTorontoOntarioCanada
| | - Alan Anticevic
- Department of PsychiatryYale UniversityNew HavenConnecticutUSA
| | - Anil K. Malhotra
- Psychiatry Research, The Zucker Hillside HospitalGlen CoveNew YorkUSA
- Institute of Behavioral Science, Feinstein Institutes for Medical ResearchManhassetNew YorkUSA
- Donald and Barbara Zucker School of Medicine at Hofstra/NorthwellHempsteadNew YorkUSA
| | - Aristotle N. Voineskos
- Center for Addiction and Mental HealthCampbell Family Mental Health ResearchTorontoOntarioCanada
- Department of PsychiatryUniversity of TorontoTorontoOntarioUSA
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Yang CC, Totzek JF, Lepage M, Lavigne KM. Sex differences in cognition and structural covariance-based morphometric connectivity: evidence from 28,000+ UK Biobank participants. Cereb Cortex 2023; 33:10341-10354. [PMID: 37557917 DOI: 10.1093/cercor/bhad286] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/12/2022] [Revised: 07/06/2023] [Accepted: 07/08/2023] [Indexed: 08/11/2023] Open
Abstract
There is robust evidence for sex differences in domain-specific cognition, where females typically show an advantage for verbal memory, whereas males tend to perform better in spatial memory. Sex differences in brain connectivity are well documented and may provide insight into these differences. In this study, we examined sex differences in cognition and structural covariance, as an index of morphometric connectivity, of a large healthy sample (n = 28,821) from the UK Biobank. Using T1-weighted magnetic resonance imaging scans and regional cortical thickness values, we applied jackknife bias estimation and graph theory to obtain subject-specific measures of structural covariance, hypothesizing that sex-related differences in brain network global efficiency, or overall covariance, would underlie cognitive differences. As predicted, females demonstrated better verbal memory and males showed a spatial memory advantage. Females also demonstrated faster processing speed, with no observed sex difference in executive functioning. Males showed higher global efficiency, as well as higher regional covariance (nodal strengths) in both hemispheres relative to females. Furthermore, higher global efficiency in males mediated sex differences in verbal memory and processing speed. Findings contribute to an improved understanding of how biological sex and differences in cognition are related to morphometric connectivity as derived from graph-theoretic methods.
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Affiliation(s)
- Crystal C Yang
- Department of Psychology, McGill University, Montréal, QC H4H 1R3, Canada
| | - Jana F Totzek
- Faculty of Psychology and Neuroscience, Maastricht University, Maastricht, 6211 LK, Netherlands
- Department of Psychiatry, McGill University, Montréal, QC H4H 1R3, Canada
- Douglas Research Centre, Montréal, QC, H4H 1R3, Canada
| | - Martin Lepage
- Department of Psychology, McGill University, Montréal, QC H4H 1R3, Canada
- Department of Psychiatry, McGill University, Montréal, QC H4H 1R3, Canada
- Douglas Research Centre, Montréal, QC, H4H 1R3, Canada
| | - Katie M Lavigne
- Department of Psychiatry, McGill University, Montréal, QC H4H 1R3, Canada
- Douglas Research Centre, Montréal, QC, H4H 1R3, Canada
- Montreal Neurological Institute-Hospital, McGill University, Montréal, QC H4H 1R3, Canada
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Han S, Zheng R, Li S, Zhou B, Jiang Y, Fang K, Wei Y, Pang J, Li H, Zhang Y, Chen Y, Cheng J. Resolving heterogeneity in depression using individualized structural covariance network analysis. Psychol Med 2023; 53:5312-5321. [PMID: 35959558 DOI: 10.1017/s0033291722002380] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 11/06/2022]
Abstract
BACKGROUND Elucidating individual aberrance is a critical first step toward precision medicine for heterogeneous disorders such as depression. The neuropathology of depression is related to abnormal inter-regional structural covariance indicating a brain maturational disruption. However, most studies focus on group-level structural covariance aberrance and ignore the interindividual heterogeneity. For that reason, we aimed to identify individualized structural covariance aberrance with the help of individualized differential structural covariance network (IDSCN) analysis. METHODS T1-weighted anatomical images of 195 first-episode untreated patients with depression and matched healthy controls (n = 78) were acquired. We obtained IDSCN for each patient and identified subtypes of depression based on shared differential edges. RESULTS As a result, patients with depression demonstrated tremendous heterogeneity in the distribution of differential structural covariance edges. Despite this heterogeneity, altered edges within subcortical-cerebellum network were often shared by most of the patients. Two robust neuroanatomical subtypes were identified. Specifically, patients in subtype 1 often shared decreased motor network-related edges. Patients in subtype 2 often shared decreased subcortical-cerebellum network-related edges. Functional annotation further revealed that differential edges in subtype 2 were mainly implicated in reward/motivation-related functional terms. CONCLUSIONS In conclusion, we investigated individualized differential structural covariance and identified that decreased edges within subcortical-cerebellum network are often shared by patients with depression. The identified two subtypes provide new insights into taxonomy and facilitate potential clues to precision diagnosis and treatment of depression.
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Affiliation(s)
- Shaoqiang Han
- Department of Magnetic Resonance Imaging, the First Affiliated Hospital of Zhengzhou University, Zhengzhou, China
- Key Laboratory for Functional Magnetic Resonance Imaging and Molecular Imaging of Henan Province, Zhengzhou, China
- Engineering Technology Research Center for Detection and Application of Brain Function of Henan Province, Zhengzhou, China
- Engineering Research Center of Medical Imaging Intelligent Diagnosis and Treatment of Henan Province, Zhengzhou, China
- Key Laboratory of Magnetic Resonance and Brain Function of Henan Province, Zhengzhou, China
- Key Laboratory of Brain Function and Cognitive Magnetic Resonance Imaging of Zhengzhou, Zhengzhou, China
- Key Laboratory of Imaging Intelligence Research Medicine of Henan Province, Zhengzhou, China
- Henan Engineering Research Center of Brain Function Development and Application, Zhengzhou, China
| | - Ruiping Zheng
- Department of Magnetic Resonance Imaging, the First Affiliated Hospital of Zhengzhou University, Zhengzhou, China
- Key Laboratory for Functional Magnetic Resonance Imaging and Molecular Imaging of Henan Province, Zhengzhou, China
- Engineering Technology Research Center for Detection and Application of Brain Function of Henan Province, Zhengzhou, China
- Engineering Research Center of Medical Imaging Intelligent Diagnosis and Treatment of Henan Province, Zhengzhou, China
- Key Laboratory of Magnetic Resonance and Brain Function of Henan Province, Zhengzhou, China
- Key Laboratory of Brain Function and Cognitive Magnetic Resonance Imaging of Zhengzhou, Zhengzhou, China
- Key Laboratory of Imaging Intelligence Research Medicine of Henan Province, Zhengzhou, China
- Henan Engineering Research Center of Brain Function Development and Application, Zhengzhou, China
| | - Shuying Li
- Department of Psychiatry, the First Affiliated Hospital of Zhengzhou University, Zhengzhou, China
| | - Bingqian Zhou
- Department of Magnetic Resonance Imaging, the First Affiliated Hospital of Zhengzhou University, Zhengzhou, China
- Key Laboratory for Functional Magnetic Resonance Imaging and Molecular Imaging of Henan Province, Zhengzhou, China
- Engineering Technology Research Center for Detection and Application of Brain Function of Henan Province, Zhengzhou, China
- Engineering Research Center of Medical Imaging Intelligent Diagnosis and Treatment of Henan Province, Zhengzhou, China
- Key Laboratory of Magnetic Resonance and Brain Function of Henan Province, Zhengzhou, China
- Key Laboratory of Brain Function and Cognitive Magnetic Resonance Imaging of Zhengzhou, Zhengzhou, China
- Key Laboratory of Imaging Intelligence Research Medicine of Henan Province, Zhengzhou, China
- Henan Engineering Research Center of Brain Function Development and Application, Zhengzhou, China
| | - Yu Jiang
- Department of Magnetic Resonance Imaging, the First Affiliated Hospital of Zhengzhou University, Zhengzhou, China
- Key Laboratory for Functional Magnetic Resonance Imaging and Molecular Imaging of Henan Province, Zhengzhou, China
- Engineering Technology Research Center for Detection and Application of Brain Function of Henan Province, Zhengzhou, China
- Engineering Research Center of Medical Imaging Intelligent Diagnosis and Treatment of Henan Province, Zhengzhou, China
- Key Laboratory of Magnetic Resonance and Brain Function of Henan Province, Zhengzhou, China
- Key Laboratory of Brain Function and Cognitive Magnetic Resonance Imaging of Zhengzhou, Zhengzhou, China
- Key Laboratory of Imaging Intelligence Research Medicine of Henan Province, Zhengzhou, China
- Henan Engineering Research Center of Brain Function Development and Application, Zhengzhou, China
| | - Keke Fang
- Department of Pharmacy, Affiliated Cancer Hospital of Zhengzhou University, Henan Cancer Hospital, Zhengzhou, China
| | - Yarui Wei
- Department of Magnetic Resonance Imaging, the First Affiliated Hospital of Zhengzhou University, Zhengzhou, China
- Key Laboratory for Functional Magnetic Resonance Imaging and Molecular Imaging of Henan Province, Zhengzhou, China
- Engineering Technology Research Center for Detection and Application of Brain Function of Henan Province, Zhengzhou, China
- Engineering Research Center of Medical Imaging Intelligent Diagnosis and Treatment of Henan Province, Zhengzhou, China
- Key Laboratory of Magnetic Resonance and Brain Function of Henan Province, Zhengzhou, China
- Key Laboratory of Brain Function and Cognitive Magnetic Resonance Imaging of Zhengzhou, Zhengzhou, China
- Key Laboratory of Imaging Intelligence Research Medicine of Henan Province, Zhengzhou, China
- Henan Engineering Research Center of Brain Function Development and Application, Zhengzhou, China
| | - Jianyue Pang
- Department of Psychiatry, the First Affiliated Hospital of Zhengzhou University, Zhengzhou, China
| | - Hengfen Li
- Department of Psychiatry, the First Affiliated Hospital of Zhengzhou University, Zhengzhou, China
| | - Yong Zhang
- Department of Magnetic Resonance Imaging, the First Affiliated Hospital of Zhengzhou University, Zhengzhou, China
- Key Laboratory for Functional Magnetic Resonance Imaging and Molecular Imaging of Henan Province, Zhengzhou, China
- Engineering Technology Research Center for Detection and Application of Brain Function of Henan Province, Zhengzhou, China
- Engineering Research Center of Medical Imaging Intelligent Diagnosis and Treatment of Henan Province, Zhengzhou, China
- Key Laboratory of Magnetic Resonance and Brain Function of Henan Province, Zhengzhou, China
- Key Laboratory of Brain Function and Cognitive Magnetic Resonance Imaging of Zhengzhou, Zhengzhou, China
- Key Laboratory of Imaging Intelligence Research Medicine of Henan Province, Zhengzhou, China
- Henan Engineering Research Center of Brain Function Development and Application, Zhengzhou, China
| | - Yuan Chen
- Department of Magnetic Resonance Imaging, the First Affiliated Hospital of Zhengzhou University, Zhengzhou, China
- Key Laboratory for Functional Magnetic Resonance Imaging and Molecular Imaging of Henan Province, Zhengzhou, China
- Engineering Technology Research Center for Detection and Application of Brain Function of Henan Province, Zhengzhou, China
- Engineering Research Center of Medical Imaging Intelligent Diagnosis and Treatment of Henan Province, Zhengzhou, China
- Key Laboratory of Magnetic Resonance and Brain Function of Henan Province, Zhengzhou, China
- Key Laboratory of Brain Function and Cognitive Magnetic Resonance Imaging of Zhengzhou, Zhengzhou, China
- Key Laboratory of Imaging Intelligence Research Medicine of Henan Province, Zhengzhou, China
- Henan Engineering Research Center of Brain Function Development and Application, Zhengzhou, China
| | - Jingliang Cheng
- Department of Magnetic Resonance Imaging, the First Affiliated Hospital of Zhengzhou University, Zhengzhou, China
- Key Laboratory for Functional Magnetic Resonance Imaging and Molecular Imaging of Henan Province, Zhengzhou, China
- Engineering Technology Research Center for Detection and Application of Brain Function of Henan Province, Zhengzhou, China
- Engineering Research Center of Medical Imaging Intelligent Diagnosis and Treatment of Henan Province, Zhengzhou, China
- Key Laboratory of Magnetic Resonance and Brain Function of Henan Province, Zhengzhou, China
- Key Laboratory of Brain Function and Cognitive Magnetic Resonance Imaging of Zhengzhou, Zhengzhou, China
- Key Laboratory of Imaging Intelligence Research Medicine of Henan Province, Zhengzhou, China
- Henan Engineering Research Center of Brain Function Development and Application, Zhengzhou, China
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Wang W, Kang Y, Niu X, Zhang Z, Li S, Gao X, Zhang M, Cheng J, Zhang Y. Connectome-based predictive modeling of smoking severity using individualized structural covariance network in smokers. Front Neurosci 2023; 17:1227422. [PMID: 37547147 PMCID: PMC10400777 DOI: 10.3389/fnins.2023.1227422] [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: 05/23/2023] [Accepted: 07/04/2023] [Indexed: 08/08/2023] Open
Abstract
Introduction Abnormal interactions among distributed brain systems are implicated in the mechanisms of nicotine addiction. However, the relationship between the structural covariance network, a measure of brain connectivity, and smoking severity remains unclear. To fill this gap, this study aimed to investigate the relationship between structural covariance network and smoking severity in smokers. Methods A total of 101 male smokers and 51 male non-smokers were recruited, and they underwent a T1-weighted anatomical image scan. First, an individualized structural covariance network was derived via a jackknife-bias estimation procedure for each participant. Then, a data-driven machine learning method called connectome-based predictive modeling (CPM) was conducted to infer smoking severity measured with Fagerström Test for Nicotine Dependence (FTND) scores using an individualized structural covariance network. The performance of CPM was evaluated using the leave-one-out cross-validation and a permutation testing. Results As a result, CPM identified the smoking severity-related structural covariance network, as indicated by a significant correlation between predicted and actual FTND scores (r = 0.23, permutation p = 0.020). Identified networks comprised of edges mainly located between the subcortical-cerebellum network and networks including the frontoparietal default model and motor and visual networks. Discussion These results identified smoking severity-related structural covariance networks and provided a new insight into the neural underpinnings of smoking severity.
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Li M, Deng W, Li Y, Zhao L, Ma X, Yu H, Li X, Meng Y, Wang Q, Du X, Sham PC, Palaniyappan L, Li T. Ameliorative patterns of grey matter in patients with first-episode and treatment-naïve schizophrenia. Psychol Med 2023; 53:3500-3510. [PMID: 35164887 PMCID: PMC10277763 DOI: 10.1017/s0033291722000058] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/28/2021] [Revised: 12/30/2021] [Accepted: 01/05/2022] [Indexed: 02/05/2023]
Abstract
BACKGROUND Grey matter (GM) reduction is a consistent observation in established late stages of schizophrenia, but patients in the untreated early stages of illness display an increase as well as a decrease in GM distribution relative to healthy controls (HC). The relative excess of GM may indicate putative compensatory responses, though to date its relevance is unclear. METHODS 343 first-episode treatment-naïve patients with schizophrenia (FES) and 342 HC were recruited. Multivariate source-based morphometry was performed to identify covarying 'networks' of grey matter concentration (GMC). Neurocognitive scores using the Cambridge Neuropsychological Test Automated Battery (CANTAB) and symptom burden using the Positive and Negative Symptoms Scale (PANSS) were obtained. Bivariate linear relationships between GMC and cognition/symptoms were studied. RESULTS Compared to healthy subjects, FES had prominently lower GMC in two components; the first consists of the anterior insula, inferior frontal gyrus, anterior cingulate and the second component with the superior temporal gyrus, precuneus, inferior/superior parietal lobule, cuneus, and lingual gyrus. Higher GMC was seen in adjacent areas of the middle and superior temporal gyrus, middle frontal gyrus, inferior parietal cortex and putamen. Greater GMC of this component was associated with lower duration of untreated psychosis, less severe positive symptoms and better performance on cognitive tests. CONCLUSIONS In untreated stages of schizophrenia, both a distributed lower and higher GMC is observable. While the higher GMC is relatively modest, it occurs across frontoparietal, temporal and subcortical regions in association with reduced illness burden suggesting a compensatory role for higher GMC in the early stages of schizophrenia.
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Affiliation(s)
- Mingli Li
- Mental Health Center and Psychiatric Laboratory, State Key Laboratory of Biotherapy, West China Hospital of Sichuan University, Chengdu, China
| | - Wei Deng
- Mental Health Center and Psychiatric Laboratory, State Key Laboratory of Biotherapy, West China Hospital of Sichuan University, Chengdu, China
- Affiliated Mental Health Center & Hangzhou Seventh People's Hospital, Zhejiang University School of Medicine, Hangzhou, Zhejiang, China
| | - Yinfei Li
- Mental Health Center and Psychiatric Laboratory, State Key Laboratory of Biotherapy, West China Hospital of Sichuan University, Chengdu, China
| | - Liansheng Zhao
- Mental Health Center and Psychiatric Laboratory, State Key Laboratory of Biotherapy, West China Hospital of Sichuan University, Chengdu, China
| | - Xiaohong Ma
- Mental Health Center and Psychiatric Laboratory, State Key Laboratory of Biotherapy, West China Hospital of Sichuan University, Chengdu, China
| | - Hua Yu
- Mental Health Center and Psychiatric Laboratory, State Key Laboratory of Biotherapy, West China Hospital of Sichuan University, Chengdu, China
| | - Xiaojing Li
- Mental Health Center and Psychiatric Laboratory, State Key Laboratory of Biotherapy, West China Hospital of Sichuan University, Chengdu, China
| | - Yajing Meng
- Mental Health Center and Psychiatric Laboratory, State Key Laboratory of Biotherapy, West China Hospital of Sichuan University, Chengdu, China
| | - Qiang Wang
- Mental Health Center and Psychiatric Laboratory, State Key Laboratory of Biotherapy, West China Hospital of Sichuan University, Chengdu, China
| | - Xiangdong Du
- Suzhou Psychiatry Hospital, Affiliated Guangji Hospital of Soochow University, Suzhou, 215137, Jiangsu, China
| | - Pak Chung Sham
- Centre for Genomic Sciences and State Key Laboratory in Cognitive and Brain Sciences, LKS Faculty of Medicine, The University of Hong Kong, Pokfulam, Hong Kong, China
| | - Lena Palaniyappan
- Robarts Research Institute & The Brain and Mind Institute, University of Western Ontario, London, Ontario, Canada
- Department of Psychiatry, University of Western Ontario, London, Ontario, Canada
- Lawson Health Research Institute, London, Ontario, Canada
| | - Tao Li
- Mental Health Center and Psychiatric Laboratory, State Key Laboratory of Biotherapy, West China Hospital of Sichuan University, Chengdu, China
- Affiliated Mental Health Center & Hangzhou Seventh People's Hospital, Zhejiang University School of Medicine, Hangzhou, Zhejiang, China
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Psychotic disorders as a framework for precision psychiatry. Nat Rev Neurol 2023; 19:221-234. [PMID: 36879033 DOI: 10.1038/s41582-023-00779-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 01/24/2023] [Indexed: 03/08/2023]
Abstract
People with psychotic disorders can show marked interindividual variations in the onset of illness, responses to treatment and relapse, but they receive broadly similar clinical care. Precision psychiatry is an approach that aims to stratify people with a given disorder according to different clinical outcomes and tailor treatment to their individual needs. At present, interindividual differences in outcomes of psychotic disorders are difficult to predict on the basis of clinical assessment alone. Therefore, current research in psychosis seeks to build models that predict outcomes by integrating clinical information with a range of biological measures. Here, we review recent progress in the application of precision psychiatry to psychotic disorders and consider the challenges associated with implementing this approach in clinical practice.
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Kitajima K, Tamura S, Sasabayashi D, Nakajima S, Iwata Y, Ueno F, Takai Y, Takahashi J, Caravaggio F, Mar W, Torres-Carmona E, Noda Y, Gerretsen P, Luca VD, Mimura M, Hirano S, Nakao T, Onitsuka T, Remington G, Graff-Guerrero A, Hirano Y. Decreased cortical gyrification and surface area in the left medial parietal cortex in patients with treatment-resistant and ultratreatment-resistant schizophrenia. Psychiatry Clin Neurosci 2023; 77:2-11. [PMID: 36165228 PMCID: PMC10092309 DOI: 10.1111/pcn.13482] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/22/2022] [Revised: 09/11/2022] [Accepted: 09/13/2022] [Indexed: 01/06/2023]
Abstract
AIM Validating the vulnerabilities and pathologies underlying treatment-resistant schizophrenia (TRS) is an important challenge in optimizing treatment. Gyrification and surface area (SA), reflecting neurodevelopmental features, have been linked to genetic vulnerability to schizophrenia. The aim of this study was to identify gyrification and SA abnormalities specific to TRS. METHODS We analyzed 3T magnetic resonance imaging findings of 24 healthy controls (HCs), 20 responders to first-line antipsychotics (FL-Resp), and 41 patients with TRS, including 19 clozapine responders (CLZ-Resp) and 22 FL- and clozapine-resistant patients (patients with ultratreatment-resistant schizophrenia [URS]). The local gyrification index (LGI) and associated SA were analyzed across groups. Diagnostic accuracy was verified by receiver operating characteristic curve analysis. RESULTS Both CLZ-Resp and URS had lower LGI values than HCs (P = 0.041, Hedges g [gH ] = 0.75; P = 0.013, gH = 0.96) and FL-Resp (P = 0.007, gH = 1.00; P = 0.002, gH = 1.31) in the left medial parietal cortex (Lt-MPC). In addition, both CLZ-Resp and URS had lower SA in the Lt-MPC than FL-Resp (P < 0.001, gH = 1.22; P < 0.001, gH = 1.75). LGI and SA were positively correlated in non-TRS (FL-Resp) (ρ = 0.64, P = 0.008) and TRS (CLZ-Resp + URS) (ρ = 0.60, P < 0.001). The areas under the receiver operating characteristic curve for non-TRS versus TRS with LGI and SA in the Lt-MPC were 0.79 and 0.85, respectively. SA in the Lt-MPC was inversely correlated with negative symptoms (ρ = -0.40, P = 0.018) and clozapine plasma levels (ρ = -0.35, P = 0.042) in TRS. CONCLUSION LGI and SA in the Lt-MPC, a functional hub in the default-mode network, were abnormally reduced in TRS compared with non-TRS. Thus, altered LGI and SA in the Lt-MPC might be structural features associated with genetic vulnerability to TRS.
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Affiliation(s)
- Kazutoshi Kitajima
- Department of Neuropsychiatry, Graduate School of Medical Sciences, Kyushu University, Fukuoka, Japan
| | - Shunsuke Tamura
- Department of Neuropsychiatry, Graduate School of Medical Sciences, Kyushu University, Fukuoka, Japan
| | - Daiki Sasabayashi
- Department of Neuropsychiatry, University of Toyama Graduate School of Medicine and Pharmaceutical Sciences, Toyama, Japan.,Research Center for Idling Brain Science, University of Toyama, Toyama, Japan
| | - Shinichiro Nakajima
- Department of Neuropsychiatry, School of Medicine, Keio University, Tokyo, Japan.,Research Imaging Centre, Centre for Addiction and Mental Health (CAMH), Toronto, Ontario, Canada
| | - Yusuke Iwata
- Research Imaging Centre, Centre for Addiction and Mental Health (CAMH), Toronto, Ontario, Canada.,Department of Neuropsychiatry, University of Yamanashi Faculty of Medicine, Yamanashi, Japan
| | - Fumihiko Ueno
- Research Imaging Centre, Centre for Addiction and Mental Health (CAMH), Toronto, Ontario, Canada
| | - Yoshifumi Takai
- Department of Neuropsychiatry, Graduate School of Medical Sciences, Kyushu University, Fukuoka, Japan
| | - Junichi Takahashi
- Department of Neuropsychiatry, Graduate School of Medical Sciences, Kyushu University, Fukuoka, Japan.,Department of Neuropsychiatry, National Hospital Organization Kyushu Medical Center, Fukuoka, Japan
| | - Fernando Caravaggio
- Research Imaging Centre, Centre for Addiction and Mental Health (CAMH), Toronto, Ontario, Canada
| | - Wanna Mar
- Research Imaging Centre, Centre for Addiction and Mental Health (CAMH), Toronto, Ontario, Canada
| | - Edgardo Torres-Carmona
- Research Imaging Centre, Centre for Addiction and Mental Health (CAMH), Toronto, Ontario, Canada.,Department of Psychiatry, University of Toronto, Toronto, Ontario, Canada
| | - Yoshihiro Noda
- Department of Neuropsychiatry, School of Medicine, Keio University, Tokyo, Japan
| | - Philip Gerretsen
- Research Imaging Centre, Centre for Addiction and Mental Health (CAMH), Toronto, Ontario, Canada.,Department of Psychiatry, University of Toronto, Toronto, Ontario, Canada
| | - Vincenzo de Luca
- Research Imaging Centre, Centre for Addiction and Mental Health (CAMH), Toronto, Ontario, Canada.,Department of Psychiatry, University of Toronto, Toronto, Ontario, Canada
| | - Masaru Mimura
- Department of Neuropsychiatry, School of Medicine, Keio University, Tokyo, Japan
| | - Shogo Hirano
- Department of Neuropsychiatry, Graduate School of Medical Sciences, Kyushu University, Fukuoka, Japan
| | - Tomohiro Nakao
- Department of Neuropsychiatry, Graduate School of Medical Sciences, Kyushu University, Fukuoka, Japan
| | - Toshiaki Onitsuka
- Department of Neuropsychiatry, Graduate School of Medical Sciences, Kyushu University, Fukuoka, Japan
| | - Gary Remington
- Research Imaging Centre, Centre for Addiction and Mental Health (CAMH), Toronto, Ontario, Canada.,Department of Psychiatry, University of Toronto, Toronto, Ontario, Canada
| | - Ariel Graff-Guerrero
- Research Imaging Centre, Centre for Addiction and Mental Health (CAMH), Toronto, Ontario, Canada.,Department of Psychiatry, University of Toronto, Toronto, Ontario, Canada
| | - Yoji Hirano
- Department of Neuropsychiatry, Graduate School of Medical Sciences, Kyushu University, Fukuoka, Japan.,Institute of Industrial Science, The University of Tokyo, Tokyo, Japan
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9
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Cheng P, Liu Z, Yang J, Sun F, Fan Z, Yang J. Decreased integration of default-mode network during a working memory task in schizophrenia with severe attention deficits. Front Cell Neurosci 2022; 16:1006797. [PMID: 36425664 PMCID: PMC9679280 DOI: 10.3389/fncel.2022.1006797] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/29/2022] [Accepted: 10/18/2022] [Indexed: 11/10/2022] Open
Abstract
Background Working memory (WM) and attention deficits are both important features of schizophrenia. WM is closely related to attention, for it acted as an important characteristic in activating and manipulating WM. However, the knowledge of neural mechanisms underlying the relationship between WM and attention deficits in schizophrenia is poorly investigated. Methods Graph theory was used to examine the network topology at the whole-brain and large-scale network levels among 125 schizophrenia patients with different severity of attention deficits (65 mild attention deficits; 46 moderate attention deficits; and 14 severe attention deficits) and 53 healthy controls (HCs) during an N-back WM task. These analyses were repeated in the same participants during the resting state. Results In the WM task, there were omnibus differences in small-worldness and normalized clustering coefficient at a whole-brain level and normalized characterized path length of the default-mode network (DMN) among all groups. Post hoc analysis further indicated that all patient groups showed increased small-worldness and normalized clustering coefficient of the whole brain compared with HCs, and schizophrenia with severe attention deficits showed increased normalized characterized path length of the DMN compared with schizophrenia with mild attention deficits and HCs. However, these observations were not persisted under the resting state. Further correlation analyses indicated that the increased normalized characterized path length of the DMN was correlated with more severe attentional deficits and poorer accuracy of the WM task. Conclusion Our research demonstrated that, compared with the schizophrenia patients with less attention deficits, disrupted integration of the DMN may more particularly underlie the WM deficits in schizophrenia patients with severe attention deficits.
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Affiliation(s)
- Peng Cheng
- Department of Psychiatry, The Second Xiangya Hospital of Central South University, Changsha, China
- National Clinical Research Center for Mental Disorders, Changsha, China
| | - Zhening Liu
- Department of Psychiatry, The Second Xiangya Hospital of Central South University, Changsha, China
- National Clinical Research Center for Mental Disorders, Changsha, China
- *Correspondence: Zhening Liu,
| | - Jun Yang
- Department of Psychiatry, The Second Xiangya Hospital of Central South University, Changsha, China
- National Clinical Research Center for Mental Disorders, Changsha, China
| | - Fuping Sun
- Department of Psychiatry, The Second Xiangya Hospital of Central South University, Changsha, China
- National Clinical Research Center for Mental Disorders, Changsha, China
| | - Zebin Fan
- Department of Psychiatry, The Second Xiangya Hospital of Central South University, Changsha, China
- National Clinical Research Center for Mental Disorders, Changsha, China
- Zebin Fan,
| | - Jie Yang
- Department of Psychiatry, The Second Xiangya Hospital of Central South University, Changsha, China
- National Clinical Research Center for Mental Disorders, Changsha, China
- Jie Yang,
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10
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Chen C, Liu Z, Xi C, Tan W, Fan Z, Cheng Y, Yang J, Palaniyappan L, Yang J. Multimetric structural covariance in first-episode major depressive disorder: a graph theoretical analysis. J Psychiatry Neurosci 2022; 47:E176-E185. [PMID: 35508328 PMCID: PMC9074807 DOI: 10.1503/jpn.210204] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/21/2021] [Revised: 02/15/2022] [Accepted: 03/12/2022] [Indexed: 11/25/2022] Open
Abstract
BACKGROUND Abnormalities of cortical morphology have been consistently reported in major depressive disorder (MDD), with widespread focal alterations in cortical thickness, surface area and gyrification. However, it is unclear whether these distributed focal changes disrupt the system-level architecture (topology) of brain morphology in MDD. If present, such a topological disruption might explain the mechanisms that underlie altered cortical morphology in MDD. METHODS Seventy-six patients with first-episode MDD (33 male, 43 female) and 66 healthy controls (32 male, 34 female) underwent structural MRI scans. We calculated cortical indices, including cortical thickness, surface area and local gyrification index, using FreeSurfer. We constructed morphological covariance networks using the 3 cortical indices separately, and we analyzed the topological properties of these group-level morphological covariance networks using graph theoretical approaches. RESULTS Topological differences between patients with first-episode MDD and healthy controls were restricted to the thickness-based network. We found a significant decrease in global efficiency but an increase in local efficiency of the left superior frontal gyrus and the right paracentral lobule in patients with first-episode MDD. When we simulated targeted lesions affecting the most highly connected nodes, the thickness-based networks in patients with first-episode MDD disintegrated more rapidly than those in healthy controls. LIMITATIONS Our sample of patients with first-episode MDD has limited generalizability to patients with chronic and recurrent MDD. CONCLUSION A systems-level disruption in cortical thickness (but not surface area or gyrification) occurs in patients with first-episode MDD.
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Affiliation(s)
| | | | | | | | | | | | - Jun Yang
- From the Department of Psychiatry, Second Xiangya Hospital of Central South University, Changsha, China (Chen, Liu, Xi, Tan, Fan, Cheng, Jun Yang, Jie Yang); the National Clinical Research Centre for Mental Disorders, Changsha, China (Chen, Liu, Xi, Tan, Fan, Cheng, Jun Yang, Jie Yang); the Department of Psychiatry, University of Western Ontario, London, Ont. (Palaniyappan); the Robarts Research Institute, University of Western Ontario, London, Ont. (Palaniyappan); the Lawson Health Research Institute, London, Ont. (Palaniyappan); the Douglas Mental Health University Institute, Department of Psychiatry, McGill University, Montréal, Quebec (Palaniyappan)
| | - Lena Palaniyappan
- From the Department of Psychiatry, Second Xiangya Hospital of Central South University, Changsha, China (Chen, Liu, Xi, Tan, Fan, Cheng, Jun Yang, Jie Yang); the National Clinical Research Centre for Mental Disorders, Changsha, China (Chen, Liu, Xi, Tan, Fan, Cheng, Jun Yang, Jie Yang); the Department of Psychiatry, University of Western Ontario, London, Ont. (Palaniyappan); the Robarts Research Institute, University of Western Ontario, London, Ont. (Palaniyappan); the Lawson Health Research Institute, London, Ont. (Palaniyappan); the Douglas Mental Health University Institute, Department of Psychiatry, McGill University, Montréal, Quebec (Palaniyappan)
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11
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Yang J, Hellerstein DJ, Chen Y, McGrath PJ, Stewart JW, Peterson BS, Wang Z. Serotonin-norepinephrine reuptake inhibitor antidepressant effects on regional connectivity of the thalamus in persistent depressive disorder: evidence from two randomized, double-blind, placebo-controlled clinical trials. Brain Commun 2022; 4:fcac100. [PMID: 35592490 PMCID: PMC9113244 DOI: 10.1093/braincomms/fcac100] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/15/2021] [Revised: 02/02/2022] [Accepted: 04/12/2022] [Indexed: 11/13/2022] Open
Abstract
Previous neuroimaging studies have shown that serotonin-norepinephrine reuptake inhibitor antidepressants alter functional activity in large expanses of brain regions. However, it is not clear how these regions are systemically organized on a connectome level with specific topological properties, which may be crucial to revealing neural mechanisms underlying serotonin-norepinephrine reuptake inhibitor treatment of persistent depressive disorder. To investigate the effect of serotonin-norepinephrine reuptake inhibitor antidepressants on brain functional connectome reconfiguration in persistent depressive disorder and whether this reconfiguration promotes the improvement of clinical symptoms, we combined resting-state functional magnetic resonance imaging (fMRI) scans acquired in two randomized, double-blind, placebo-controlled trial studies of serotonin-norepinephrine reuptake inhibitor antidepressant treatment of patients with persistent depressive disorder. One was a randomized, double-blind, placebo-controlled trial of 10-week duloxetine medication treatment, which included 17 patients in duloxetine group and 17 patients in placebo group (ClinicalTrials.gov Identifier: NCT00360724); the other one was a randomized, double-blind, placebo-controlled trial of 12-week desvenlafaxine medication treatment, which included 16 patients in desvenlafaxine group and 15 patients in placebo group (ClinicalTrials.gov Identifier: NCT01537068). The 24-item Hamilton Depression Rating Scale was used to measure clinical symptoms, and graph theory was employed to examine serotonin-norepinephrine reuptake inhibitor antidepressant treatment effects on the topological properties of whole-brain functional connectome of patients with persistent depressive disorder. We adopted a hierarchical strategy to examine the topological property changes caused by serotonin-norepinephrine reuptake inhibitor antidepressant treatment, calculated their small-worldness, global integration, local segregation and nodal clustering coefficient in turn. Linear regression analysis was used to test associations of treatment, graph properties changes and clinical symptom response. Symptom scores were more significantly reduced after antidepressant than placebo administration (η 2 = 0.18). There was a treatment-by-time effect that optimized the functional connectome in a small-world manner, with increased global integration and increased nodal clustering coefficient in the bilateral thalamus (left thalamus η 2 = 0.21; right thalamus η 2 = 0.23). The nodal clustering coefficient increment of the right thalamus (ratio = 29.86; 95% confidence interval, -4.007 to -0.207) partially mediated the relationship between treatment and symptom improvement, and symptom improvement partially mediated (ratio = 21.21; 95% confidence interval, 0.0243-0.444) the relationship between treatment and nodal clustering coefficient increments of the right thalamus. Our study may indicate a putative mutually reinforcing association between nodal clustering coefficient increment of the right thalamus and symptom improvement from serotonin-norepinephrine reuptake inhibitor antidepressant treatments with duloxetine or desvenlafaxine.
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Affiliation(s)
- Jie Yang
- Department of Psychiatry, and National Clinical Research Center for Mental Disorders, The Second Xiangya Hospital of Central South University, Changsha 410011, Hunan, China
- Department of Depression Evaluation Service, New York State Psychiatric Institute, 1051 Riverside Drive, Unit #51, New York, NY 10032, USA
| | - David J. Hellerstein
- Department of Depression Evaluation Service, New York State Psychiatric Institute, 1051 Riverside Drive, Unit #51, New York, NY 10032, USA
- Vagelos College of Physicians and Surgeons, Columbia University, New York, NY 10032, USA
| | - Ying Chen
- Department of Depression Evaluation Service, New York State Psychiatric Institute, 1051 Riverside Drive, Unit #51, New York, NY 10032, USA
- Vagelos College of Physicians and Surgeons, Columbia University, New York, NY 10032, USA
- Mailman School of Public Health, Columbia University, New York, NY 10032, USA
| | - Patrick J. McGrath
- Department of Depression Evaluation Service, New York State Psychiatric Institute, 1051 Riverside Drive, Unit #51, New York, NY 10032, USA
- Vagelos College of Physicians and Surgeons, Columbia University, New York, NY 10032, USA
| | - Jonathan W. Stewart
- Department of Depression Evaluation Service, New York State Psychiatric Institute, 1051 Riverside Drive, Unit #51, New York, NY 10032, USA
- Vagelos College of Physicians and Surgeons, Columbia University, New York, NY 10032, USA
| | - Bradley S. Peterson
- Institute for the Developing Mind, Children’s Hospital Los Angeles, Los Angeles, CA 90027, USA
- Keck School of Medicine, University of Southern California, Los Angeles, CA 90089-9021, USA
| | - Zhishun Wang
- Department of Depression Evaluation Service, New York State Psychiatric Institute, 1051 Riverside Drive, Unit #51, New York, NY 10032, USA
- Vagelos College of Physicians and Surgeons, Columbia University, New York, NY 10032, USA
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12
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Gregorich M, Melograna F, Sunqvist M, Michiels S, Van Steen K, Heinze G. Individual-specific networks for prediction modelling – A scoping review of methods. BMC Med Res Methodol 2022; 22:62. [PMID: 35249534 PMCID: PMC8898441 DOI: 10.1186/s12874-022-01544-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/27/2021] [Accepted: 02/11/2022] [Indexed: 11/10/2022] Open
Abstract
Background Recent advances in biotechnology enable the acquisition of high-dimensional data on individuals, posing challenges for prediction models which traditionally use covariates such as clinical patient characteristics. Alternative forms of covariate representations for the features derived from these modern data modalities should be considered that can utilize their intrinsic interconnection. The connectivity information between these features can be represented as an individual-specific network defined by a set of nodes and edges, the strength of which can vary from individual to individual. Global or local graph-theoretical features describing the network may constitute potential prognostic biomarkers instead of or in addition to traditional covariates and may replace the often unsuccessful search for individual biomarkers in a high-dimensional predictor space. Methods We conducted a scoping review to identify, collate and critically appraise the state-of-art in the use of individual-specific networks for prediction modelling in medicine and applied health research, published during 2000–2020 in the electronic databases PubMed, Scopus and Embase. Results Our scoping review revealed the main application areas namely neurology and pathopsychology, followed by cancer research, cardiology and pathology (N = 148). Network construction was mainly based on Pearson correlation coefficients of repeated measurements, but also alternative approaches (e.g. partial correlation, visibility graphs) were found. For covariates measured only once per individual, network construction was mostly based on quantifying an individual’s contribution to the overall group-level structure. Despite the multitude of identified methodological approaches for individual-specific network inference, the number of studies that were intended to enable the prediction of clinical outcomes for future individuals was quite limited, and most of the models served as proof of concept that network characteristics can in principle be useful for prediction. Conclusion The current body of research clearly demonstrates the value of individual-specific network analysis for prediction modelling, but it has not yet been considered as a general tool outside the current areas of application. More methodological research is still needed on well-founded strategies for network inference, especially on adequate network sparsification and outcome-guided graph-theoretical feature extraction and selection, and on how networks can be exploited efficiently for prediction modelling. Supplementary Information The online version contains supplementary material available at 10.1186/s12874-022-01544-6.
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13
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Lai H, Kong X, Zhao Y, Pan N, Zhang X, He M, Wang S, Gong Q. Patterns of a structural covariance network associated with dispositional optimism during late adolescence. Neuroimage 2022; 251:119009. [PMID: 35182752 DOI: 10.1016/j.neuroimage.2022.119009] [Citation(s) in RCA: 17] [Impact Index Per Article: 8.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/12/2021] [Revised: 01/28/2022] [Accepted: 02/15/2022] [Indexed: 02/08/2023] Open
Abstract
Dispositional optimism (hereinafter, optimism), as a vital character strength, reflects the tendency to hold generalized positive expectancies for future outcomes. A great number of studies have consistently shown the importance of optimism to a spectrum of physical and mental health outcomes. However, less attention has been given to the intrinsic neurodevelopmental patterns associated with interindividual differences in optimism. Here, we investigated this important question in a large sample comprising 231 healthy adolescents (16-20 years old) via structural magnetic resonance imaging and behavioral tests. We constructed individual structural covariance networks based on cortical gyrification using a recent novel approach combining probability density estimation and Kullback-Leibler divergence and estimated global (global efficiency, local efficiency and small-worldness) and regional (betweenness centrality) properties of these constructed networks using graph theoretical analysis. Partial correlations adjusted for age, sex and estimated total intracranial volume showed that optimism was positively related to global and local efficiency but not small-worldness. Partial least squares correlations indicated that optimism was positively linked to a pronounced betweenness centrality pattern, in which twelve cognition-, emotion-, and motivation-related regions made robust and reliable contributions. These findings remained basically consistent after additionally controlling for family socioeconomic status and showed significant correlations with optimism scores from 2.5 years before, which replicated the main findings. The current work, for the first time, delineated characteristics of the cortical gyrification covariance network associated with optimism, extending previous neurobiological understandings of optimism, which may navigate the development of interventions on a neural network level aimed at raising optimism.
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Affiliation(s)
- Han Lai
- Huaxi MR Research Center (HMRRC), Department of Radiology, West China Hospital of Sichuan University, Chengdu, China; Research Unit of Psychoradiology, Chinese Academy of Medical Sciences, Chengdu, China; Functional & Molecular Imaging Key Laboratory of Sichuan Province, West China Hospital of Sichuan University, Chengdu, China; Department of Psychology, Army Medical University, Chongqing, China
| | - Xiangzhen Kong
- Department of Psychology and Behavioral Sciences, Zhejiang University, Hangzhou, China
| | - Yajun Zhao
- School of Education and Psychology, Southwest Minzu University, Chengdu, China
| | - Nanfang Pan
- Huaxi MR Research Center (HMRRC), Department of Radiology, West China Hospital of Sichuan University, Chengdu, China; Research Unit of Psychoradiology, Chinese Academy of Medical Sciences, Chengdu, China; Functional & Molecular Imaging Key Laboratory of Sichuan Province, West China Hospital of Sichuan University, Chengdu, China
| | - Xun Zhang
- Huaxi MR Research Center (HMRRC), Department of Radiology, West China Hospital of Sichuan University, Chengdu, China; Research Unit of Psychoradiology, Chinese Academy of Medical Sciences, Chengdu, China; Functional & Molecular Imaging Key Laboratory of Sichuan Province, West China Hospital of Sichuan University, Chengdu, China
| | - Min He
- Huaxi MR Research Center (HMRRC), Department of Radiology, West China Hospital of Sichuan University, Chengdu, China; Research Unit of Psychoradiology, Chinese Academy of Medical Sciences, Chengdu, China; Functional & Molecular Imaging Key Laboratory of Sichuan Province, West China Hospital of Sichuan University, Chengdu, China
| | - Song Wang
- Huaxi MR Research Center (HMRRC), Department of Radiology, West China Hospital of Sichuan University, Chengdu, China; Research Unit of Psychoradiology, Chinese Academy of Medical Sciences, Chengdu, China; Functional & Molecular Imaging Key Laboratory of Sichuan Province, West China Hospital of Sichuan University, Chengdu, China.
| | - Qiyong Gong
- Huaxi MR Research Center (HMRRC), Department of Radiology, West China Hospital of Sichuan University, Chengdu, China; Research Unit of Psychoradiology, Chinese Academy of Medical Sciences, Chengdu, China; Functional & Molecular Imaging Key Laboratory of Sichuan Province, West China Hospital of Sichuan University, Chengdu, China.
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14
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Sasabayashi D, Takayanagi Y, Takahashi T, Furuichi A, Kobayashi H, Noguchi K, Suzuki M. Increased brain gyrification and subsequent relapse in patients with first-episode schizophrenia. Front Psychiatry 2022; 13:937605. [PMID: 36032231 PMCID: PMC9406142 DOI: 10.3389/fpsyt.2022.937605] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/06/2022] [Accepted: 06/30/2022] [Indexed: 11/13/2022] Open
Abstract
Most schizophrenia patients experience psychotic relapses, which may compromise long-term outcome. However, it is difficult to objectively assess the actual risk of relapse for each patient as the biological changes underlying relapse remain unknown. The present study used magnetic resonance imaging (MRI) to investigate the relationship between brain gyrification pattern and subsequent relapse in patients with first-episode schizophrenia. The subjects consisted of 19 patients with and 33 patients without relapse during a 3-year clinical follow-up after baseline MRI scanning. Using FreeSurfer software, we compared the local gyrification index (LGI) between the relapsed and non-relapsed groups. In the relapsed group, we also explored the relationship among LGI and the number of relapses and time to first relapse after MRI scanning. Relapsed patients exhibited a significantly higher LGI in the bilateral parietal and left occipital areas than non-relapsed patients. In addition, the time to first relapse was negatively correlated with LGI in the right inferior temporal cortex. These findings suggest that increased LGI in the temporo-parieto-occipital regions in first-episode schizophrenia patients may be a potential prognostic biomarker that reflects relapse susceptibility in the early course of the illness.
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Affiliation(s)
- Daiki Sasabayashi
- Department of Neuropsychiatry, University of Toyama Graduate School of Medicine and Pharmaceutical Sciences, Toyama, Japan.,Research Center for Idling Brain Science, University of Toyama, Toyama, Japan
| | - Yoichiro Takayanagi
- Department of Neuropsychiatry, University of Toyama Graduate School of Medicine and Pharmaceutical Sciences, Toyama, Japan.,Arisawabashi Hospital, Toyama, Japan
| | - Tsutomu Takahashi
- Department of Neuropsychiatry, University of Toyama Graduate School of Medicine and Pharmaceutical Sciences, Toyama, Japan.,Research Center for Idling Brain Science, University of Toyama, Toyama, Japan
| | - Atsushi Furuichi
- Department of Neuropsychiatry, University of Toyama Graduate School of Medicine and Pharmaceutical Sciences, Toyama, Japan.,Research Center for Idling Brain Science, University of Toyama, Toyama, Japan
| | - Haruko Kobayashi
- Department of Neuropsychiatry, University of Toyama Graduate School of Medicine and Pharmaceutical Sciences, Toyama, Japan.,Research Center for Idling Brain Science, University of Toyama, Toyama, Japan
| | - Kyo Noguchi
- Department of Radiology, University of Toyama Graduate School of Medicine and Pharmaceutical Sciences, Toyama, Japan
| | - Michio Suzuki
- Department of Neuropsychiatry, University of Toyama Graduate School of Medicine and Pharmaceutical Sciences, Toyama, Japan.,Research Center for Idling Brain Science, University of Toyama, Toyama, Japan
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15
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Tavares V, Vassos E, Marquand A, Stone J, Valli I, Barker GJ, Ferreira H, Prata D. Prediction of transition to psychosis from an at-risk mental state using structural neuroimaging, genetic, and environmental data. Front Psychiatry 2022; 13:1086038. [PMID: 36741573 PMCID: PMC9892839 DOI: 10.3389/fpsyt.2022.1086038] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/31/2022] [Accepted: 12/29/2022] [Indexed: 01/20/2023] Open
Abstract
INTRODUCTION Psychosis is usually preceded by a prodromal phase in which patients are clinically identified as being at in an "At Risk Mental State" (ARMS). A few studies have demonstrated the feasibility of predicting psychosis transition from an ARMS using structural magnetic resonance imaging (sMRI) data and machine learning (ML) methods. However, the reliability of these findings is unclear due to possible sampling bias. Moreover, the value of genetic and environmental data in predicting transition to psychosis from an ARMS is yet to be explored. METHODS In this study we aimed to predict transition to psychosis from an ARMS using a combination of ML, sMRI, genome-wide genotypes, and environmental risk factors as predictors, in a sample drawn from a pool of 246 ARMS subjects (60 of whom later transitioned to psychosis). First, the modality-specific values in predicting transition to psychosis were evaluated using several: (a) feature types; (b) feature manipulation strategies; (c) ML algorithms; (d) cross-validation strategies, as well as sample balancing and bootstrapping. Subsequently, the modalities whose at least 60% of the classification models showed an balanced accuracy (BAC) statistically better than chance level were included in a multimodal classification model. RESULTS AND DISCUSSION Results showed that none of the modalities alone, i.e., neuroimaging, genetic or environmental data, could predict psychosis from an ARMS statistically better than chance and, as such, no multimodal classification model was trained/tested. These results suggest that the value of structural MRI data and genome-wide genotypes in predicting psychosis from an ARMS, which has been fostered by previous evidence, should be reconsidered.
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Affiliation(s)
- Vânia Tavares
- Instituto de Biofísica e Engenharia Biomédica, Faculdade de Ciências, Universidade de Lisboa, Lisbon, Portugal.,Faculdade de Medicina, Universidade de Lisboa, Lisbon, Portugal
| | - Evangelos Vassos
- Social, Genetic and Developmental Psychiatry Centre, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, United Kingdom.,National Institute for Health Research Maudsley Biomedical Research Centre, South London and Maudsley National Health System Trust, London, United Kingdom
| | - Andre Marquand
- Donders Centre for Cognitive Neuroimaging, Donders Institute for Brain, Cognition and Behaviour, Radboud University, Nijmegen, Netherlands.,Department of Cognitive Neuroscience, Radboud University Medical Centre, Nijmegen, Netherlands
| | - James Stone
- Brighton and Sussex Medical School, University of Sussex, Brighton, United Kingdom
| | - Isabel Valli
- Department of Psychosis Studies, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, United Kingdom.,Institut d'Investigacions Biomèdiques August Pi i Sunyer, University of Barcelona, Barcelona, Spain
| | - Gareth J Barker
- Department of Neuroimaging, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, United Kingdom
| | - Hugo Ferreira
- Instituto de Biofísica e Engenharia Biomédica, Faculdade de Ciências, Universidade de Lisboa, Lisbon, Portugal
| | - Diana Prata
- Instituto de Biofísica e Engenharia Biomédica, Faculdade de Ciências, Universidade de Lisboa, Lisbon, Portugal.,Department of Old Age Psychiatry, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, United Kingdom
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16
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Resolving heterogeneity in schizophrenia through a novel systems approach to brain structure: individualized structural covariance network analysis. Mol Psychiatry 2021; 26:7719-7731. [PMID: 34316005 DOI: 10.1038/s41380-021-01229-4] [Citation(s) in RCA: 46] [Impact Index Per Article: 15.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/17/2021] [Revised: 06/15/2021] [Accepted: 07/05/2021] [Indexed: 12/12/2022]
Abstract
Reliable mapping of system-level individual differences is a critical first step toward precision medicine for complex disorders such as schizophrenia. Disrupted structural covariance indicates a system-level brain maturational disruption in schizophrenia. However, most studies examine structural covariance at the group level. This prevents subject-level inferences. Here, we introduce a Network Template Perturbation approach to construct individual differential structural covariance network (IDSCN) using regional gray-matter volume. IDSCN quantifies how structural covariance between two nodes in a patient deviates from the normative covariance in healthy subjects. We analyzed T1 images from 1287 subjects, including 107 first-episode (drug-naive) patients and 71 controls in the discovery datasets and established robustness in 213 first-episode (drug-naive), 294 chronic, 99 clinical high-risk patients, and 494 controls from the replication datasets. Patients with schizophrenia were highly variable in their altered structural covariance edges; the number of altered edges was related to severity of hallucinations. Despite this variability, a subset of covariance edges, including the left hippocampus-bilateral putamen/globus pallidus edges, clustered patients into two distinct subgroups with opposing changes in covariance compared to controls, and significant differences in their anxiety and depression scores. These subgroup differences were stable across all seven datasets with meaningful genetic associations and functional annotation for the affected edges. We conclude that the underlying physiology of affective symptoms in schizophrenia involves the hippocampus and putamen/pallidum, predates disease onset, and is sufficiently consistent to resolve morphological heterogeneity throughout the illness course. The two schizophrenia subgroups identified thus have implications for the nosology and clinical treatment.
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17
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Palaniyappan L, Sabesan P, Li X, Luo Q. Schizophrenia Increases Variability of the Central Antioxidant System: A Meta-Analysis of Variance From MRS Studies of Glutathione. Front Psychiatry 2021; 12:796466. [PMID: 34916980 PMCID: PMC8669304 DOI: 10.3389/fpsyt.2021.796466] [Citation(s) in RCA: 10] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/16/2021] [Accepted: 11/02/2021] [Indexed: 11/13/2022] Open
Abstract
Patients with schizophrenia diverge in their clinical trajectories. Such diverge outcomes may result from the resilience provided by antioxidant response system centered on glutathione (GSH). Proton Magnetic Resonance Spectroscopy (1H-MRS) has enabled the precise in vivo measurement of intracortical GSH; but individual studies report highly variable results even when GSH levels are measured from the same brain region. This inconsistency could be due to the presence of distinct subgroups of schizophrenia with varying GSH-levels. At present, we do not know if schizophrenia increases the interindividual variability of intracortical GSH relative to matched healthy individuals. We reviewed all 1H-MRS GSH studies in schizophrenia focused on the Anterior Cingulate Cortex published until August 2021. We estimated the relative variability of ACC GSH levels in patients compared to control groups using the variability ratio (VR) and coefficient of variation ratio (CVR). The presence of schizophrenia significantly increases the variability of intracortical GSH in the ACC (logVR = 0.12; 95% CI: 0.03-0.21; log CVR = 0.15; 95% CI = 0.06-0.23). Insofar as increased within-group variability (heterogeneity) could result from the existence of subtypes, our results call for a careful examination of intracortical GSH distribution in schizophrenia to seek redox-deficient and redox-sufficient subgroups. An increase in GSH variability among patients also indicate that the within-group predictability of adaptive response to oxidative stress may be lower in schizophrenia. Uncovering the origins of this illness-related reduction in the redox system stability may provide novel treatment targets in schizophrenia.
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Affiliation(s)
- Lena Palaniyappan
- Department of Psychiatry, University of Western Ontario, London, ON, Canada
- Robarts Research Institute, London, ON, Canada
- Lawson Health Research Institute, London, ON, Canada
- Department of Medical Biophysics, University of Western Ontario, London, ON, Canada
| | | | - Xuan Li
- MOE-Key Laboratory of Computational Neuroscience and Brain-Inspired Intelligence, Institute of Science and Technology for Brain-Inspired Intelligence, Fudan University, Shanghai, China
| | - Qiang Luo
- MOE-Key Laboratory of Computational Neuroscience and Brain-Inspired Intelligence, Institute of Science and Technology for Brain-Inspired Intelligence, Fudan University, Shanghai, China
- National Clinical Research Center for Aging and Medicine at Huashan Hospital, State Key Laboratory of Medical Neurobiology and MOE Frontiers Center for Brain Science, Institutes of Brain Science and Human Phenome Institute, Fudan University, Shanghai, China
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18
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Ajnakina O, Das T, Lally J, Di Forti M, Pariante CM, Marques TR, Mondelli V, David AS, Murray RM, Palaniyappan L, Dazzan P. Structural Covariance of Cortical Gyrification at Illness Onset in Treatment Resistance: A Longitudinal Study of First-Episode Psychoses. Schizophr Bull 2021; 47:1729-1739. [PMID: 33851203 PMCID: PMC8530394 DOI: 10.1093/schbul/sbab035] [Citation(s) in RCA: 12] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 12/26/2022]
Abstract
Treatment resistance (TR) in patients with first-episode psychosis (FEP) is a major cause of disability and functional impairment, yet mechanisms underlying this severe disorder are poorly understood. As one view is that TR has neurodevelopmental roots, we investigated whether its emergence relates to disruptions in synchronized cortical maturation quantified using gyrification-based connectomes. Seventy patients with FEP evaluated at their first presentation to psychiatric services were followed up using clinical records for 4 years; of these, 17 (24.3%) met the definition of TR and 53 (75.7%) remained non-TR at 4 years. Structural MRI images were obtained within 5 weeks from first exposure to antipsychotics. Local gyrification indices were computed for 148 contiguous cortical regions using FreeSurfer; each subject's contribution to group-based structural covariance was quantified using a jack-knife procedure, providing a single deviation matrix for each subject. The latter was used to derive topological properties that were compared between TR and non-TR patients using a Functional Data Analysis approach. Compared to the non-TR patients, TR patients showed a significant reduction in small-worldness (Hedges's g = 2.09, P < .001) and a reduced clustering coefficient (Hedges's g = 1.07, P < .001) with increased length (Hedges's g = -2.17, P < .001), indicating a disruption in the organizing principles of cortical folding. The positive symptom burden was higher in patients with more pronounced small-worldness (r = .41, P = .001) across the entire sample. The trajectory of synchronized cortical development inferred from baseline MRI-based structural covariance highlights the possibility of identifying patients at high-risk of TR prospectively, based on individualized gyrification-based connectomes.
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Affiliation(s)
- Olesya Ajnakina
- Department of Biostatistics and Health Informatics, Institute of Psychiatry, Psychology & Neuroscience, King’s College London, London, UK
- Department of Behavioural Science and Health, Institute of Epidemiology and Health Care, University College London, London, UK
| | - Tushar Das
- Departments of Psychiatry & Medical Biophysics, Robarts Research Institute & Lawson Health Research Institute, University of Western Ontario, London, Ontario, Canada
| | - John Lally
- Department of Psychosis Studies, Institute of Psychiatry, Psychology & Neuroscience, King’s College London, London, UK
- Department of Psychiatry, Royal College of Surgeons in Ireland, Dublin, Ireland
- Department of Psychiatry, St Vincent’s Hospital Fairview, Dublin, Ireland
- Department of Psychiatry, Mater Misericordiae University Hospital, Dublin, Ireland
| | - Marta Di Forti
- MRC Social, Genetic & Developmental Psychiatry Centre, Institute of Psychiatry, Psychology & Neuroscience, King’s College London, London, UK
| | - Carmine M Pariante
- Department of Psychological Medicine, Institute of Psychiatry, Psychology and Neuroscience, Kings College London, London, UK
| | - Tiago Reis Marques
- Department of Psychosis Studies, Institute of Psychiatry, Psychology & Neuroscience, King’s College London, London, UK
- Psychiatric Imaging Group, MRC London Institute of Medical Sciences (LMS), Hammersmith Hospital, Imperial College London, London, UK
| | - Valeria Mondelli
- Department of Psychological Medicine, Institute of Psychiatry, Psychology and Neuroscience, Kings College London, London, UK
| | - Anthony S David
- Institute of Mental Health, University College London, London, UK
| | - Robin M Murray
- Department of Psychosis Studies, Institute of Psychiatry, Psychology & Neuroscience, King’s College London, London, UK
- Department of Psychiatry, Experimental Biomedicine and Clinical Neuroscience, University of Palermo, Palermo, Italy
| | - Lena Palaniyappan
- Departments of Psychiatry & Medical Biophysics, Robarts Research Institute & Lawson Health Research Institute, University of Western Ontario, London, Ontario, Canada
| | - Paola Dazzan
- Department of Psychosis Studies, Institute of Psychiatry, Psychology & Neuroscience, King’s College London, London, UK
- National Institute for Health Research (NIHR) Mental Health Biomedical Research Centre at South London and Maudsley NHS Foundation Trust and King’s College London, London, UK
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19
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Raballo A, Poletti M, Preti A. Individualized Diagnostic and Prognostic Models for Psychosis Risk Syndromes: Do Not Underestimate Antipsychotic Exposure. Biol Psychiatry 2021; 90:e33-e35. [PMID: 34001370 DOI: 10.1016/j.biopsych.2021.01.020] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/21/2021] [Accepted: 03/12/2021] [Indexed: 10/21/2022]
Affiliation(s)
- Andrea Raballo
- Section of Psychiatry, Clinical Psychology and Rehabilitation, Department of Medicine, University of Perugia, Italy; Center for Translational, Phenomenological and Developmental Psychopathology, Perugia University Hospital, Perugia, Italy.
| | - Michele Poletti
- Child and Adolescent Neuropsychiatry Service, Department of Mental Health and Pathological Addiction, Azienda USL-IRCCS di Reggio Emilia, Reggio Emilia, Italy
| | - Antonio Preti
- Department of Neuroscience, University of Turin, Turin, Italy
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20
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Sanfelici R, Ruef A, Antonucci LA, Penzel N, Sotiras A, Dong MS, Urquijo-Castro M, Wenzel J, Kambeitz-Ilankovic L, Hettwer MD, Ruhrmann S, Chisholm K, Riecher-Rössler A, Falkai P, Pantelis C, Salokangas RKR, Lencer R, Bertolino A, Kambeitz J, Meisenzahl E, Borgwardt S, Brambilla P, Wood SJ, Upthegrove R, Schultze-Lutter F, Koutsouleris N, Dwyer DB. Novel Gyrification Networks Reveal Links with Psychiatric Risk Factors in Early Illness. Cereb Cortex 2021; 32:1625-1636. [PMID: 34519351 DOI: 10.1093/cercor/bhab288] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/18/2021] [Revised: 07/12/2021] [Accepted: 07/13/2021] [Indexed: 12/13/2022] Open
Abstract
Adult gyrification provides a window into coordinated early neurodevelopment when disruptions predispose individuals to psychiatric illness. We hypothesized that the echoes of such disruptions should be observed within structural gyrification networks in early psychiatric illness that would demonstrate associations with developmentally relevant variables rather than specific psychiatric symptoms. We employed a new data-driven method (Orthogonal Projective Non-Negative Matrix Factorization) to delineate novel gyrification-based networks of structural covariance in 308 healthy controls. Gyrification within the networks was then compared to 713 patients with recent onset psychosis or depression, and at clinical high-risk. Associations with diagnosis, symptoms, cognition, and functioning were investigated using linear models. Results demonstrated 18 novel gyrification networks in controls as verified by internal and external validation. Gyrification was reduced in patients in temporal-insular, lateral occipital, and lateral fronto-parietal networks (pFDR < 0.01) and was not moderated by illness group. Higher gyrification was associated with better cognitive performance and lifetime role functioning, but not with symptoms. The findings demonstrated that gyrification can be parsed into novel brain networks that highlight generalized illness effects linked to developmental vulnerability. When combined, our study widens the window into the etiology of psychiatric risk and its expression in adulthood.
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Affiliation(s)
- Rachele Sanfelici
- Department of Psychiatry and Psychotherapy, Ludwig-Maximilian University, Munich, 80336, Germany.,Max Planck School of Cognition, Leipzig, 04103, Germany
| | - Anne Ruef
- Department of Psychiatry and Psychotherapy, Ludwig-Maximilian University, Munich, 80336, Germany
| | - Linda A Antonucci
- Department of Psychiatry and Psychotherapy, Ludwig-Maximilian University, Munich, 80336, Germany.,Department of Basic Medical Science, Neuroscience and Sense Organs, University of Bari Aldo Moro, Bari, 70124, Italy
| | - Nora Penzel
- Department of Psychiatry and Psychotherapy, Faculty of Medicine and University Hospital, University of Cologne, Cologne, 50937, Germany
| | - Aristeidis Sotiras
- Department of Radiology and Institute of Informatics, Washington University in St. Luis, st. Luis, MO63110, USA
| | - Mark Sen Dong
- Department of Psychiatry and Psychotherapy, Ludwig-Maximilian University, Munich, 80336, Germany
| | - Maria Urquijo-Castro
- Department of Psychiatry and Psychotherapy, Ludwig-Maximilian University, Munich, 80336, Germany
| | - Julian Wenzel
- Department of Psychiatry and Psychotherapy, Faculty of Medicine and University Hospital, University of Cologne, Cologne, 50937, Germany
| | - Lana Kambeitz-Ilankovic
- Department of Psychiatry and Psychotherapy, Ludwig-Maximilian University, Munich, 80336, Germany.,Department of Psychiatry and Psychotherapy, Faculty of Medicine and University Hospital, University of Cologne, Cologne, 50937, Germany
| | | | - Stephan Ruhrmann
- Department of Psychiatry and Psychotherapy, Faculty of Medicine and University Hospital, University of Cologne, Cologne, 50937, Germany
| | - Katharine Chisholm
- Institute for Mental Health, University of Birmingham, Birmingham, B15 2TT, UK.,Department of Psychology, Aston University, Birmingham, B4 7ET, UK
| | | | - Peter Falkai
- Department of Psychiatry and Psychotherapy, Ludwig-Maximilian University, Munich, 80336, Germany.,Max-Planck Institute of Psychiatry, Munich, 80804, Germany
| | - Christos Pantelis
- Melbourne Neuropsychiatry Centrem University of Melbourne & Melbourne Health, Melbourne, 3053, Australia
| | | | - Rebekka Lencer
- Department of Psychiatry and Psychotherapy, University of Münster, Münster, 48149, Germany.,Department of Psychiatry and Psychotherapy, University of Lübeck, Lübeck, 23538, Germany
| | - Alessandro Bertolino
- Department of Basic Medical Science, Neuroscience and Sense Organs, University of Bari Aldo Moro, Bari, 70124, Italy
| | - Joseph Kambeitz
- Department of Psychiatry and Psychotherapy, Faculty of Medicine and University Hospital, University of Cologne, Cologne, 50937, Germany
| | - Eva Meisenzahl
- Department of Psychiatry and Psychotherapy, Medical Faculty, Heinrich-Heine University, Düsseldorf, 40629, Germany
| | - Stefan Borgwardt
- Department of Psychiatry and Psychotherapy, University of Lübeck, Lübeck, 23538, Germany.,Department of Psychiatry (Psychiatric University Hospital, UPK), University of Basel, Basel, 4002, Switzerland
| | - Paolo Brambilla
- Department of Neurosciences and Mental Health, Fondazione IRCCS Ca' Grande Ospedale Maggiore Policlinico, Milano, 20122, Italy.,Department of Pathophysiology and Transplantation, University of Milan, Milan, 20122, Italy
| | - Stephen J Wood
- Centre for Youth Mental Health, University of Melbourne, Melbourne, 3052, Australia.,Orygen, Melbourne, 3052, Australia.,School of Psychology, University of Birmingham, Birmingham, B15 2TT, UK
| | - Rachel Upthegrove
- Institute for Mental Health, University of Birmingham, Birmingham, B15 2TT, UK.,Early Intervention Service, Birmingham Women's and Children's NHS foundation Trust, Birmingham, B4 6NH, UK
| | - Frauke Schultze-Lutter
- Department of Psychiatry and Psychotherapy, Medical Faculty, Heinrich-Heine University, Düsseldorf, 40629, Germany.,Department of Psychology and Mental Health, Faculty of Psychology, Airlangga University, Surubaya, 60286, Indonesia.,University Hospital of Child and Adolescent Psychiatry and Psychotherapy, University of Bern, Bern, 3000, Switzerland
| | - Nikolaos Koutsouleris
- Department of Psychiatry and Psychotherapy, Ludwig-Maximilian University, Munich, 80336, Germany.,Max-Planck Institute of Psychiatry, Munich, 80804, Germany.,Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, SE5 8AF, UK
| | - Dominic B Dwyer
- Department of Psychiatry and Psychotherapy, Ludwig-Maximilian University, Munich, 80336, Germany
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21
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Associations between long-term psychosis risk, probabilistic category learning, and attenuated psychotic symptoms with cortical surface morphometry. Brain Imaging Behav 2021; 16:91-106. [PMID: 34218406 DOI: 10.1007/s11682-021-00479-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 05/25/2021] [Indexed: 10/20/2022]
Abstract
Neuroimaging studies have consistently found structural cortical abnormalities in individuals with schizophrenia, especially in structural hubs. However, it is unclear what abnormalities predate psychosis onset and whether abnormalities are related to behavioral performance and symptoms associated with psychosis risk. Using surface-based morphometry, we examined cortical volume, gyrification, and thickness in a psychosis risk group at long-term risk for developing a psychotic disorder (n = 18; i.e., extreme positive schizotypy plus interview-rated attenuated psychotic symptoms [APS]) and control group (n = 19). Overall, the psychosis risk group exhibited cortical abnormalities in multiple structural hub regions, with abnormalities associated with poorer probabilistic category learning, a behavioral measure strongly associated with psychosis risk. For instance, the psychosis risk group had hypogyria in a right posterior midcingulate cortical hub and left superior parietal cortical hub, as well as decreased volume in a right pericalcarine hub. Morphometric measures in all of these regions were also associated with poorer probabilistic category learning. In addition to decreased right pericalcarine volume, the psychosis risk group exhibited a number of other structural abnormalities in visual network structural hub regions, consistent with previous evidence of visual perception deficits in psychosis risk. Further, severity of APS hallucinations, delusional ideation, and suspiciousness/persecutory ideas were associated with gyrification abnormalities, with all domains associated with hypogyria of the right lateral orbitofrontal cortex. Thus, current results suggest that structural abnormalities, especially in structural hubs, are present in psychosis risk and are associated both with poor learning on a psychosis risk-related task and with APS severity.
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22
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Multimodal prognosis of negative symptom severity in individuals at increased risk of developing psychosis. Transl Psychiatry 2021; 11:312. [PMID: 34031362 PMCID: PMC8144430 DOI: 10.1038/s41398-021-01409-4] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/13/2020] [Revised: 04/12/2021] [Accepted: 04/30/2021] [Indexed: 12/21/2022] Open
Abstract
Negative symptoms occur frequently in individuals at clinical high risk (CHR) for psychosis and contribute to functional impairments. The aim of this study was to predict negative symptom severity in CHR after 9 months. Predictive models either included baseline negative symptoms measured with the Structured Interview for Psychosis-Risk Syndromes (SIPS-N), whole-brain gyrification, or both to forecast negative symptoms of at least moderate severity in 94 CHR. We also conducted sequential risk stratification to stratify CHR into different risk groups based on the SIPS-N and gyrification model. Additionally, we assessed the models' ability to predict functional outcomes in CHR and their transdiagnostic generalizability to predict negative symptoms in 96 patients with recent-onset psychosis (ROP) and 97 patients with recent-onset depression (ROD). Baseline SIPS-N and gyrification predicted moderate/severe negative symptoms with significant balanced accuracies of 68 and 62%, while the combined model achieved 73% accuracy. Sequential risk stratification stratified CHR into a high (83%), medium (40-64%), and low (19%) risk group regarding their risk of having moderate/severe negative symptoms at 9 months follow-up. The baseline SIPS-N model was also able to predict social (61%), but not role functioning (59%) at above-chance accuracies, whereas the gyrification model achieved significant accuracies in predicting both social (76%) and role (74%) functioning in CHR. Finally, only the baseline SIPS-N model showed transdiagnostic generalization to ROP (63%). This study delivers a multimodal prognostic model to identify those CHR with a clinically relevant negative symptom severity and functional impairments, potentially requiring further therapeutic consideration.
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23
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Takahashi T, Sasabayashi D, Takayanagi Y, Higuchi Y, Mizukami Y, Nishiyama S, Furuichi A, Kido M, Pham TV, Kobayashi H, Noguchi K, Suzuki M. Heschl's Gyrus Duplication Pattern in Individuals at Risk of Developing Psychosis and Patients With Schizophrenia. Front Behav Neurosci 2021; 15:647069. [PMID: 33958991 PMCID: PMC8093503 DOI: 10.3389/fnbeh.2021.647069] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/29/2020] [Accepted: 03/29/2021] [Indexed: 11/13/2022] Open
Abstract
An increased prevalence of duplicated Heschl’s gyrus (HG), which may reflect an early neurodevelopmental pathology, has been reported in schizophrenia (Sz). However, it currently remains unclear whether individuals at risk of psychosis exhibit similar brain morphological characteristics. This magnetic resonance imaging study investigated the distribution of HG gyrification patterns [i.e., single HG, common stem duplication (CSD), and complete posterior duplication (CPD)] and their relationship with clinical characteristics in 57 individuals with an at-risk mental state (ARMS) [of whom 5 (8.8%) later developed Sz], 63 patients with Sz, and 61 healthy comparisons. The prevalence of duplicated HG patterns (i.e., CSD or CPD) bilaterally was significantly higher in the ARMS and Sz groups than in the controls, whereas no significant differences were observed in HG patterns between these groups. The left CSD pattern, particularly in the Sz group, was associated with a verbal fluency deficit. In the ARMS group, left CSD pattern was related to a more severe general psychopathology. The present results suggest that an altered gyrification pattern on the superior temporal plane reflects vulnerability factors associated with Sz, which may also contribute to the clinical features of high-risk individuals, even without the onset of psychosis.
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Affiliation(s)
- Tsutomu Takahashi
- Department of Neuropsychiatry, Graduate School of Medicine and Pharmaceutical Sciences, University of Toyama, Toyama, Japan.,Research Center for Idling Brain Science, University of Toyama, Toyama, Japan
| | - Daiki Sasabayashi
- Department of Neuropsychiatry, Graduate School of Medicine and Pharmaceutical Sciences, University of Toyama, Toyama, Japan.,Research Center for Idling Brain Science, University of Toyama, Toyama, Japan
| | - Yoichiro Takayanagi
- Department of Neuropsychiatry, Graduate School of Medicine and Pharmaceutical Sciences, University of Toyama, Toyama, Japan.,Arisawabashi Hospital, Toyama, Japan
| | - Yuko Higuchi
- Department of Neuropsychiatry, Graduate School of Medicine and Pharmaceutical Sciences, University of Toyama, Toyama, Japan.,Research Center for Idling Brain Science, University of Toyama, Toyama, Japan
| | - Yuko Mizukami
- Department of Neuropsychiatry, Graduate School of Medicine and Pharmaceutical Sciences, University of Toyama, Toyama, Japan
| | - Shimako Nishiyama
- Department of Neuropsychiatry, Graduate School of Medicine and Pharmaceutical Sciences, University of Toyama, Toyama, Japan.,Health Administration Center, Faculty of Education and Research Promotion, Academic Assembly, University of Toyama, Toyama, Japan
| | - Atsushi Furuichi
- Department of Neuropsychiatry, Graduate School of Medicine and Pharmaceutical Sciences, University of Toyama, Toyama, Japan.,Research Center for Idling Brain Science, University of Toyama, Toyama, Japan
| | - Mikio Kido
- Department of Neuropsychiatry, Graduate School of Medicine and Pharmaceutical Sciences, University of Toyama, Toyama, Japan.,Research Center for Idling Brain Science, University of Toyama, Toyama, Japan
| | - Tien Viet Pham
- Department of Neuropsychiatry, Graduate School of Medicine and Pharmaceutical Sciences, University of Toyama, Toyama, Japan.,Research Center for Idling Brain Science, University of Toyama, Toyama, Japan
| | - Haruko Kobayashi
- Department of Neuropsychiatry, Graduate School of Medicine and Pharmaceutical Sciences, University of Toyama, Toyama, Japan.,Research Center for Idling Brain Science, University of Toyama, Toyama, Japan
| | - Kyo Noguchi
- Department of Radiology, Graduate School of Medicine and Pharmaceutical Sciences, University of Toyama, Toyama, Japan
| | - Michio Suzuki
- Department of Neuropsychiatry, Graduate School of Medicine and Pharmaceutical Sciences, University of Toyama, Toyama, Japan.,Research Center for Idling Brain Science, University of Toyama, Toyama, Japan
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24
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Sasabayashi D, Takahashi T, Takayanagi Y, Suzuki M. Anomalous brain gyrification patterns in major psychiatric disorders: a systematic review and transdiagnostic integration. Transl Psychiatry 2021; 11:176. [PMID: 33731700 PMCID: PMC7969935 DOI: 10.1038/s41398-021-01297-8] [Citation(s) in RCA: 37] [Impact Index Per Article: 12.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/23/2020] [Revised: 02/14/2021] [Accepted: 02/24/2021] [Indexed: 01/31/2023] Open
Abstract
Anomalous patterns of brain gyrification have been reported in major psychiatric disorders, presumably reflecting their neurodevelopmental pathology. However, previous reports presented conflicting results of patients having hyper-, hypo-, or normal gyrification patterns and lacking in transdiagnostic consideration. In this article, we systematically review previous magnetic resonance imaging studies of brain gyrification in schizophrenia, bipolar disorder, major depressive disorder, and autism spectrum disorder at varying illness stages, highlighting the gyral pattern trajectory for each disorder. Patients with each psychiatric disorder may exhibit deviated primary gyri formation under neurodevelopmental genetic control in their fetal life and infancy, and then exhibit higher-order gyral changes due to mechanical stress from active brain changes (e.g., progressive reduction of gray matter volume and white matter integrity) thereafter, representing diversely altered pattern trajectories from those of healthy controls. Based on the patterns of local connectivity and changes in neurodevelopmental gene expression in major psychiatric disorders, we propose an overarching model that spans the diagnoses to explain how deviated gyral pattern trajectories map onto clinical manifestations (e.g., psychosis, mood dysregulation, and cognitive impairments), focusing on the common and distinct gyral pattern changes across the disorders in addition to their correlations with specific clinical features. This comprehensive understanding of the role of brain gyrification pattern on the pathophysiology may help to optimize the prediction and diagnosis of psychiatric disorders using objective biomarkers, as well as provide a novel nosology informed by neural circuits beyond the current descriptive diagnostics.
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Affiliation(s)
- Daiki Sasabayashi
- Department of Neuropsychiatry, University of Toyama Graduate School of Medicine and Pharmaceutical Sciences, Toyama, Japan. .,Research Center for Idling Brain Science, University of Toyama, Toyama, Japan.
| | - Tsutomu Takahashi
- grid.267346.20000 0001 2171 836XDepartment of Neuropsychiatry, University of Toyama Graduate School of Medicine and Pharmaceutical Sciences, Toyama, Japan ,grid.267346.20000 0001 2171 836XResearch Center for Idling Brain Science, University of Toyama, Toyama, Japan
| | - Yoichiro Takayanagi
- grid.267346.20000 0001 2171 836XDepartment of Neuropsychiatry, University of Toyama Graduate School of Medicine and Pharmaceutical Sciences, Toyama, Japan ,Arisawabashi Hospital, Toyama, Japan
| | - Michio Suzuki
- grid.267346.20000 0001 2171 836XDepartment of Neuropsychiatry, University of Toyama Graduate School of Medicine and Pharmaceutical Sciences, Toyama, Japan ,grid.267346.20000 0001 2171 836XResearch Center for Idling Brain Science, University of Toyama, Toyama, Japan
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25
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Koutsouleris N, Dwyer DB, Degenhardt F, Maj C, Urquijo-Castro MF, Sanfelici R, Popovic D, Oeztuerk O, Haas SS, Weiske J, Ruef A, Kambeitz-Ilankovic L, Antonucci LA, Neufang S, Schmidt-Kraepelin C, Ruhrmann S, Penzel N, Kambeitz J, Haidl TK, Rosen M, Chisholm K, Riecher-Rössler A, Egloff L, Schmidt A, Andreou C, Hietala J, Schirmer T, Romer G, Walger P, Franscini M, Traber-Walker N, Schimmelmann BG, Flückiger R, Michel C, Rössler W, Borisov O, Krawitz PM, Heekeren K, Buechler R, Pantelis C, Falkai P, Salokangas RKR, Lencer R, Bertolino A, Borgwardt S, Noethen M, Brambilla P, Wood SJ, Upthegrove R, Schultze-Lutter F, Theodoridou A, Meisenzahl E. Multimodal Machine Learning Workflows for Prediction of Psychosis in Patients With Clinical High-Risk Syndromes and Recent-Onset Depression. JAMA Psychiatry 2021; 78:195-209. [PMID: 33263726 PMCID: PMC7711566 DOI: 10.1001/jamapsychiatry.2020.3604] [Citation(s) in RCA: 117] [Impact Index Per Article: 39.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 12/23/2022]
Abstract
IMPORTANCE Diverse models have been developed to predict psychosis in patients with clinical high-risk (CHR) states. Whether prediction can be improved by efficiently combining clinical and biological models and by broadening the risk spectrum to young patients with depressive syndromes remains unclear. OBJECTIVES To evaluate whether psychosis transition can be predicted in patients with CHR or recent-onset depression (ROD) using multimodal machine learning that optimally integrates clinical and neurocognitive data, structural magnetic resonance imaging (sMRI), and polygenic risk scores (PRS) for schizophrenia; to assess models' geographic generalizability; to test and integrate clinicians' predictions; and to maximize clinical utility by building a sequential prognostic system. DESIGN, SETTING, AND PARTICIPANTS This multisite, longitudinal prognostic study performed in 7 academic early recognition services in 5 European countries followed up patients with CHR syndromes or ROD and healthy volunteers. The referred sample of 167 patients with CHR syndromes and 167 with ROD was recruited from February 1, 2014, to May 31, 2017, of whom 26 (23 with CHR syndromes and 3 with ROD) developed psychosis. Patients with 18-month follow-up (n = 246) were used for model training and leave-one-site-out cross-validation. The remaining 88 patients with nontransition served as the validation of model specificity. Three hundred thirty-four healthy volunteers provided a normative sample for prognostic signature evaluation. Three independent Swiss projects contributed a further 45 cases with psychosis transition and 600 with nontransition for the external validation of clinical-neurocognitive, sMRI-based, and combined models. Data were analyzed from January 1, 2019, to March 31, 2020. MAIN OUTCOMES AND MEASURES Accuracy and generalizability of prognostic systems. RESULTS A total of 668 individuals (334 patients and 334 controls) were included in the analysis (mean [SD] age, 25.1 [5.8] years; 354 [53.0%] female and 314 [47.0%] male). Clinicians attained a balanced accuracy of 73.2% by effectively ruling out (specificity, 84.9%) but ineffectively ruling in (sensitivity, 61.5%) psychosis transition. In contrast, algorithms showed high sensitivity (76.0%-88.0%) but low specificity (53.5%-66.8%). A cybernetic risk calculator combining all algorithmic and human components predicted psychosis with a balanced accuracy of 85.5% (sensitivity, 84.6%; specificity, 86.4%). In comparison, an optimal prognostic workflow produced a balanced accuracy of 85.9% (sensitivity, 84.6%; specificity, 87.3%) at a much lower diagnostic burden by sequentially integrating clinical-neurocognitive, expert-based, PRS-based, and sMRI-based risk estimates as needed for the given patient. Findings were supported by good external validation results. CONCLUSIONS AND RELEVANCE These findings suggest that psychosis transition can be predicted in a broader risk spectrum by sequentially integrating algorithms' and clinicians' risk estimates. For clinical translation, the proposed workflow should undergo large-scale international validation.
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Affiliation(s)
- Nikolaos Koutsouleris
- Department of Psychiatry and Psychotherapy, Ludwig-Maximilian-University, Munich, Germany,Max-Planck Institute of Psychiatry, Munich, Germany,Institute of Psychiatry, Psychology and Neuroscience, King’s College London, London, United Kingdom
| | - Dominic B. Dwyer
- Department of Psychiatry and Psychotherapy, Ludwig-Maximilian-University, Munich, Germany
| | - Franziska Degenhardt
- Institute of Human Genetics, Rheinische Friedrich-Wilhelms-Universität Bonn, Bonn, Germany,Department of Child and Adolescent Psychiatry, Psychosomatics and Psychotherapy, University Hospital Essen, University of Duisburg-Essen, Essen, Germany
| | - Carlo Maj
- Institute of Genomic Statistics and Bioinformatics, University of Bonn, Bonn, Germany
| | | | - Rachele Sanfelici
- Department of Psychiatry and Psychotherapy, Ludwig-Maximilian-University, Munich, Germany,Max-Planck School of Cognition, Leipzig, Germany
| | - David Popovic
- Department of Psychiatry and Psychotherapy, Ludwig-Maximilian-University, Munich, Germany,International Max-Planck Research School for Translational Psychiatry, Munich, Germany
| | - Oemer Oeztuerk
- Department of Psychiatry and Psychotherapy, Ludwig-Maximilian-University, Munich, Germany,International Max-Planck Research School for Translational Psychiatry, Munich, Germany
| | - Shalaila S. Haas
- Department of Psychiatry, Icahn School of Medicine at Mount Sinai, New York, New York
| | - Johanna Weiske
- Department of Psychiatry and Psychotherapy, Ludwig-Maximilian-University, Munich, Germany
| | - Anne Ruef
- Department of Psychiatry and Psychotherapy, Ludwig-Maximilian-University, Munich, Germany
| | - Lana Kambeitz-Ilankovic
- Department of Psychiatry and Psychotherapy, Faculty of Medicine and University Hospital, University of Cologne, Cologne, Germany
| | - Linda A. Antonucci
- Department of Education, Psychology, and Communication, University of Bari Aldo Moro, Bari, Italy
| | - Susanne Neufang
- Department of Psychiatry and Psychotherapy, Medical Faculty, Heinrich-Heine University, Düsseldorf, Germany
| | | | - Stephan Ruhrmann
- Department of Psychiatry and Psychotherapy, Faculty of Medicine and University Hospital, University of Cologne, Cologne, Germany
| | - Nora Penzel
- Department of Psychiatry and Psychotherapy, Faculty of Medicine and University Hospital, University of Cologne, Cologne, Germany
| | - Joseph Kambeitz
- Department of Psychiatry and Psychotherapy, Faculty of Medicine and University Hospital, University of Cologne, Cologne, Germany
| | - Theresa K. Haidl
- Department of Psychiatry and Psychotherapy, Faculty of Medicine and University Hospital, University of Cologne, Cologne, Germany
| | - Marlene Rosen
- Department of Psychiatry and Psychotherapy, Faculty of Medicine and University Hospital, University of Cologne, Cologne, Germany
| | - Katharine Chisholm
- Institute for Mental Health, University of Birmingham, Birmingham, United Kingdom
| | - Anita Riecher-Rössler
- Department of Psychiatry, Psychiatric University Hospital, University of Basel, Switzerland
| | - Laura Egloff
- Department of Psychiatry, Psychiatric University Hospital, University of Basel, Switzerland
| | - André Schmidt
- Department of Psychiatry, Psychiatric University Hospital, University of Basel, Switzerland
| | - Christina Andreou
- Department of Psychiatry, Psychiatric University Hospital, University of Basel, Switzerland
| | - Jarmo Hietala
- Department of Psychiatry, University of Turku, Turku, Finland
| | - Timo Schirmer
- GE Healthcare GmbH (previously GE Global Research GmbH), Munich, Germany
| | - Georg Romer
- Department of Child and Adolescent Psychiatry, University of Münster, Münster, Germany
| | - Petra Walger
- Department of Child and Adolescent Psychiatry, Psychotherapy and Psychosomatics, LVR Clinic Düsseldorf, Düsseldorf, Germany
| | - Maurizia Franscini
- Department of Child and Adolescent Psychiatry and Psychotherapy, University of Zürich, Zürich, Switzerland
| | - Nina Traber-Walker
- Department of Child and Adolescent Psychiatry and Psychotherapy, University of Zürich, Zürich, Switzerland
| | - Benno G. Schimmelmann
- University Hospital of Child and Adolescent Psychiatry, University Hospital Hamburg-Eppendorf, Hamburg, Germany,University Hospital of Child and Adolescent Psychiatry and Psychotherapy, University of Bern, Bern, Switzerland
| | - Rahel Flückiger
- University Hospital of Child and Adolescent Psychiatry and Psychotherapy, University of Bern, Bern, Switzerland
| | - Chantal Michel
- University Hospital of Child and Adolescent Psychiatry and Psychotherapy, University of Bern, Bern, Switzerland
| | - Wulf Rössler
- Department of Psychiatry, Psychotherapy and Psychosomatics, University Hospital of Psychiatry Zurich, Zurich, Switzerland
| | - Oleg Borisov
- Institute of Genomic Statistics and Bioinformatics, University of Bonn, Bonn, Germany
| | - Peter M. Krawitz
- Institute of Genomic Statistics and Bioinformatics, University of Bonn, Bonn, Germany
| | - Karsten Heekeren
- Department of Psychiatry, Psychotherapy and Psychosomatics, University Hospital of Psychiatry Zurich, Zurich, Switzerland,Department of Psychiatry and Psychotherapy I, LVR Hospital Cologne, Cologne, Germany
| | - Roman Buechler
- Department of Psychiatry, Psychotherapy and Psychosomatics, University Hospital of Psychiatry Zurich, Zurich, Switzerland,Department of Neuroradiology, University Hospital of Zurich, Zurich, Switzerland
| | - Christos Pantelis
- Melbourne Neuropsychiatry Centre, University of Melbourne and Melbourne Health, Melbourne, Australia
| | - Peter Falkai
- Department of Psychiatry and Psychotherapy, Ludwig-Maximilian-University, Munich, Germany,Max-Planck Institute of Psychiatry, Munich, Germany
| | | | - Rebekka Lencer
- Department of Psychiatry and Psychotherapy, University of Münster, Münster, Germany,Department of Psychiatry and Psychotherapy, University of Lübeck, Lübeck, Germany
| | - Alessandro Bertolino
- Department of Basic Medical Science, Neuroscience and Sense Organs, University of Bari Aldo Moro, Bari, Italy
| | - Stefan Borgwardt
- Department of Psychiatry, Psychiatric University Hospital, University of Basel, Switzerland,Department of Psychiatry and Psychotherapy, University of Lübeck, Lübeck, Germany
| | - Markus Noethen
- Institute of Human Genetics, Rheinische Friedrich-Wilhelms-Universität Bonn, Bonn, Germany
| | - Paolo Brambilla
- Department of Neurosciences and Mental Health, Fondazione Istituto di Ricovero e Cura a Carattere Scientifico Ca’ Granda Ospedale Maggiore Policlinico, Milan, Italy,Department of Pathophysiology and Transplantation, University of Milan, Milan, Italy
| | - Stephen J. Wood
- Centre for Youth Mental Health, University of Melbourne, Melbourne, Australia,Orygen, the National Centre of Excellence for Youth Mental Health, Melbourne, Australia
| | - Rachel Upthegrove
- Institute for Mental Health, University of Birmingham, Birmingham, United Kingdom
| | - Frauke Schultze-Lutter
- Department of Psychiatry and Psychotherapy, Medical Faculty, Heinrich-Heine University, Düsseldorf, Germany,Department of Psychology and Mental Health, Faculty of Psychology, Airlangga University, Surabaya, Indonesia
| | - Anastasia Theodoridou
- Department of Psychiatry, Psychotherapy and Psychosomatics, University Hospital of Psychiatry Zurich, Zurich, Switzerland
| | - Eva Meisenzahl
- Department of Psychiatry and Psychotherapy, Medical Faculty, Heinrich-Heine University, Düsseldorf, Germany
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Gharehgazlou A, Richardson JD, Jetly R, Dunkley BT. Cortical gyrification morphology in PTSD: A neurobiological risk factor for severity? Neurobiol Stress 2021; 14:100299. [PMID: 33659579 PMCID: PMC7890044 DOI: 10.1016/j.ynstr.2021.100299] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/24/2020] [Revised: 01/05/2021] [Accepted: 01/18/2021] [Indexed: 12/20/2022] Open
Abstract
Post-traumatic stress disorder (PTSD) is a prevalent psychiatric disorder, particularly among military personnel and veterans. Cortical gyrification, as a specific metric derived from structural MRI, is an index of the convoluted folding and patterning of the gyri and sulci, and is thought to facilitate the efficiency of local neuronal wiring. It has the potential to act as a neurobiological risk factor for emergent psychiatric disorders – to date, it has been understudied in PTSD. Here, using a local measure of the degree of gyrification (local Gyrification Index, lGI) we investigate cortical gyrification morphology in 48 adult male soldiers with (n = 23) and without (n = 25) a PTSD diagnosis. We also examine the relation between lGI and PTSD severity within the PTSD group. General linear models yielded significant between-group differences with greater lGI found in PTSD in a cluster located in the medial occipito-parietal lobe on the left hemisphere and reduced lGI in a cluster located on the lateral surface of the parietal lobe on the right hemisphere. Brain-behaviour analyses within the PTSD group yielded significant positive associations between lGI and PTSD severity in a cluster located in the frontal cortex of the left hemisphere and scattered clusters located within all lobes of the right hemisphere. After accounting for the effects of comorbid psychiatric symptoms common in PTSD, the associations in the right hemisphere reduced to clusters only located in the frontal lobe, while the cluster in the left hemisphere remained significant. Our results suggest that atypical cortical gyrification in parietal and occipital regions may be implicated in the psychopathology of PTSD diagnosis, and properties of prefrontal gyrification associated with the emergent severity of PTSD after trauma. The importance of these regions in PTSD may be attributed to a pre-existing neurobiological risk factor, or neuromorphological changes after trauma precipitating emergent psychiatric illness. Our brain-behaviour relations provide support for the existing literature by highlighting the importance of the frontal lobe in the pathogenesis of PTSD. Future large-scale longitudinal studies including female participants may infer causal implications of atypical gyrification in PTSD and shed light on the potential effect of sex on this brain metric.
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Affiliation(s)
- Avideh Gharehgazlou
- Neurosciences & Mental Health, The Hospital for Sick Children (SickKids) Research Institute, Toronto, Ontario, Canada.,Institute of Medical Science, Faculty of Medicine, University of Toronto, Toronto, Ontario, Canada.,Bloorview Research Institute, Holland Bloorview Kids Rehabilitation Hospital, Toronto, Ontario, Canada
| | - J Don Richardson
- The MacDonald Franklin OSI Research Centre, Lawson Health Research Institute, London, Ontario, Canada.,Department of Psychiatry, Western University, London, Ontario, Canada.,Department of Psychiatry and Behavioral Neurosciences, McMaster University, Hamilton, Ontario, Canada.,Operational Stress Injury Clinic, St. Joseph's Health Care, London, Ontario, Canada
| | - Rakesh Jetly
- Canadian Forces Health Services Group HQ, Department of National Defence, Ottawa, Ontario, Canada
| | - Benjamin T Dunkley
- Neurosciences & Mental Health, The Hospital for Sick Children (SickKids) Research Institute, Toronto, Ontario, Canada.,Department of Diagnostic Imaging, The Hospital for Sick Children (SickKids), Toronto, Ontario, Canada.,Department of Medical Imaging, University of Toronto, Toronto, Ontario, Canada
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27
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Yun JY, Kim YK. Phenotype Network and Brain Structural Covariance Network of Major Depression. ADVANCES IN EXPERIMENTAL MEDICINE AND BIOLOGY 2021; 1305:3-18. [PMID: 33834391 DOI: 10.1007/978-981-33-6044-0_1] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/24/2022]
Abstract
Phenotype networks enable clinicians to elucidate the patterns of coexistence and interactions among the clinical symptoms, negative cognitive styles , neurocognitive performance, and environmental factors in major depressive disorder (MDD). Results of phenotype network approach could be used in finding the target symptoms as these are tightly connected or associated with many other phenomena within the phenotype network of MDD specifically when comorbid psychiatric disorder(s) is/are present. Further, by comparing the differential patterns of phenotype networks before and after the treatment, changing or enduring patterns of associations among the clinical phenomena in MDD have been deciphered.Brain structural covariance networks describe the inter-regional co-varying patterns of brain morphologies, and overlapping findings have been reported between the brain structural covariance network and coordinated trajectories of brain development and maturation. Intra-individual brain structural covariance illustrates the degrees of similarities among the different brain regions for how much the values of brain morphological features are deviated from those of healthy controls. Inter-individual brain structural covariance reflects the degrees of concordance among the different brain regions for the inter-individual distribution of brain morphologic values. Estimation of the graph metrics for these brain structural covariance networks uncovers the organizational profile of brain morphological variations in the whole brain and the regional distribution of brain hubs.
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Affiliation(s)
- Je-Yeon Yun
- Seoul National University Hospital, Seoul, Republic of Korea. .,Yeongeon Student Support Center, Seoul National University College of Medicine, Seoul, Republic of Korea.
| | - Yong-Ku Kim
- Department of Psychiatry, Korea University Ansan Hospital, College of Medicine, Ansan, Republic of Korea
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28
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Mongan D, Föcking M, Healy C, Susai SR, Heurich M, Wynne K, Nelson B, McGorry PD, Amminger GP, Nordentoft M, Krebs MO, Riecher-Rössler A, Bressan RA, Barrantes-Vidal N, Borgwardt S, Ruhrmann S, Sachs G, Pantelis C, van der Gaag M, de Haan L, Valmaggia L, Pollak TA, Kempton MJ, Rutten BPF, Whelan R, Cannon M, Zammit S, Cagney G, Cotter DR, McGuire P. Development of Proteomic Prediction Models for Transition to Psychotic Disorder in the Clinical High-Risk State and Psychotic Experiences in Adolescence. JAMA Psychiatry 2021; 78:77-90. [PMID: 32857162 PMCID: PMC7450406 DOI: 10.1001/jamapsychiatry.2020.2459] [Citation(s) in RCA: 46] [Impact Index Per Article: 15.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 12/20/2022]
Abstract
IMPORTANCE Biomarkers that are predictive of outcomes in individuals at risk of psychosis would facilitate individualized prognosis and stratification strategies. OBJECTIVE To investigate whether proteomic biomarkers may aid prediction of transition to psychotic disorder in the clinical high-risk (CHR) state and adolescent psychotic experiences (PEs) in the general population. DESIGN, SETTING, AND PARTICIPANTS This diagnostic study comprised 2 case-control studies nested within the European Network of National Schizophrenia Networks Studying Gene-Environment Interactions (EU-GEI) and the Avon Longitudinal Study of Parents and Children (ALSPAC). EU-GEI is an international multisite prospective study of participants at CHR referred from local mental health services. ALSPAC is a United Kingdom-based general population birth cohort. Included were EU-GEI participants who met CHR criteria at baseline and ALSPAC participants who did not report PEs at age 12 years. Data were analyzed from September 2018 to April 2020. MAIN OUTCOMES AND MEASURES In EU-GEI, transition status was assessed by the Comprehensive Assessment of At-Risk Mental States or contact with clinical services. In ALSPAC, PEs at age 18 years were assessed using the Psychosis-Like Symptoms Interview. Proteomic data were obtained from mass spectrometry of baseline plasma samples in EU-GEI and plasma samples at age 12 years in ALSPAC. Support vector machine learning algorithms were used to develop predictive models. RESULTS The EU-GEI subsample (133 participants at CHR (mean [SD] age, 22.6 [4.5] years; 68 [51.1%] male) comprised 49 (36.8%) who developed psychosis and 84 (63.2%) who did not. A model based on baseline clinical and proteomic data demonstrated excellent performance for prediction of transition outcome (area under the receiver operating characteristic curve [AUC], 0.95; positive predictive value [PPV], 75.0%; and negative predictive value [NPV], 98.6%). Functional analysis of differentially expressed proteins implicated the complement and coagulation cascade. A model based on the 10 most predictive proteins accurately predicted transition status in training (AUC, 0.99; PPV, 76.9%; and NPV, 100%) and test (AUC, 0.92; PPV, 81.8%; and NPV, 96.8%) data. The ALSPAC subsample (121 participants from the general population with plasma samples available at age 12 years (61 [50.4%] male) comprised 55 participants (45.5%) with PEs at age 18 years and 61 (50.4%) without PEs at age 18 years. A model using proteomic data at age 12 years predicted PEs at age 18 years, with an AUC of 0.74 (PPV, 67.8%; and NPV, 75.8%). CONCLUSIONS AND RELEVANCE In individuals at risk of psychosis, proteomic biomarkers may contribute to individualized prognosis and stratification strategies. These findings implicate early dysregulation of the complement and coagulation cascade in the development of psychosis outcomes.
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Affiliation(s)
- David Mongan
- Department of Psychiatry, Royal College of Surgeons in Ireland, Dublin, Ireland
| | - Melanie Föcking
- Department of Psychiatry, Royal College of Surgeons in Ireland, Dublin, Ireland
| | - Colm Healy
- Department of Psychiatry, Royal College of Surgeons in Ireland, Dublin, Ireland
| | - Subash Raj Susai
- Department of Psychiatry, Royal College of Surgeons in Ireland, Dublin, Ireland
| | - Meike Heurich
- School of Pharmacy and Pharmaceutical Sciences, Cardiff University, Cardiff, United Kingdom
| | - Kieran Wynne
- School of Biomolecular and Biomedical Science, Conway Institute, University College Dublin, Dublin, Ireland
| | - Barnaby Nelson
- Centre for Youth Mental Health, University of Melbourne, Parkville, Victoria, Australia
| | - Patrick D. McGorry
- Centre for Youth Mental Health, University of Melbourne, Parkville, Victoria, Australia
| | - G. Paul Amminger
- Centre for Youth Mental Health, University of Melbourne, Parkville, Victoria, Australia
| | - Merete Nordentoft
- Mental Health Centre Copenhagen, Copenhagen University Hospital, Copenhagen, Denmark
| | - Marie-Odile Krebs
- University Paris Descartes, Groupe Hospitalier Universitaire (GHU) Paris–Sainte Anne, Evaluation Centre for Young Adults and Adolescents (C’JAAD), Service Hospitalov–Universitaire, Institut National de la Santé et de la Recherche Medicale (INSERM) U1266, Institut de Psychiatrie (Centre National de la Recherche Scientifique [CNRS] 3557), Paris, France
| | | | - Rodrigo A. Bressan
- LiNC–Lab Interdisciplinar Neurociências Clínicas, Depto Psiquiatria, Escola Paulista de Medicina, Universidade Federal de São Paulo (UNIFESP), São Paulo, Brazil
| | - Neus Barrantes-Vidal
- Departament de Psicologia Clínica i de la Salut (Universitat Autònoma de Barcelona), Fundació Sanitària Sant Pere Claver (Spain), Spanish Mental Health Research Network (Centro de Investigación Biomédica en Red de Salud Mental [CIBERSAM]), Barcelona, Spain
| | - Stefan Borgwardt
- Department of Psychiatry, Medical Faculty, University of Basel, Basel, Switzerland,Department of Psychiatry and Psychotherapy, Translational Psychiatry Unit, University zu Lübeck, Lübeck, Germany
| | - Stephan Ruhrmann
- Department of Psychiatry and Psychotherapy, Faculty of Medicine and University Hospital Cologne, University of Cologne, Cologne, Germany
| | - Gabriele Sachs
- Department of Psychiatry and Psychotherapy, Medical University of Vienna, Vienna, Austria
| | - Christos Pantelis
- Melbourne Neuropsychiatry Centre, Department of Psychiatry, University of Melbourne and Melbourne Health, Carlton South, Victoria, Australia
| | - Mark van der Gaag
- Faculty of Behavioural and Movement Sciences, Department of Clinical Psychology and EMGO+ Institute for Health and Care Research, Vrije Universiteit (VU) University, Amsterdam, the Netherlands,Department of Psychosis Research, Parnassia Psychiatric Institute, The Hague, the Netherlands
| | - Lieuwe de Haan
- Academic Medical Centre (AMC), Academic Psychiatric Centre, Department Early Psychosis, Amsterdam, the Netherlands
| | - Lucia Valmaggia
- Institute of Psychiatry, Psychology & Neuroscience, Department of Psychology, King’s College London, London, United Kingdom
| | - Thomas A. Pollak
- Institute of Psychiatry, Psychology & Neuroscience, Department of Psychosis Studies, King’s College London, London, United Kingdom
| | - Matthew J. Kempton
- Institute of Psychiatry, Psychology & Neuroscience, Department of Psychosis Studies, King’s College London, London, United Kingdom
| | - Bart P. F. Rutten
- Department of Psychiatry and Neuropsychology, School for Mental Health and Neuroscience, Maastricht University Medical Centre, Maastricht, the Netherlands
| | - Robert Whelan
- Trinity Institute of Neuroscience, Trinity College Dublin, Dublin, Ireland
| | - Mary Cannon
- Department of Psychiatry, Royal College of Surgeons in Ireland, Dublin, Ireland
| | - Stan Zammit
- Medical Research Council (MRC) Centre for Neuropsychiatric Genetics and Genomics, Cardiff University, Cardiff, United Kingdom,Bristol Medical School, University of Bristol, Bristol, United Kingdom
| | - Gerard Cagney
- School of Biomolecular and Biomedical Science, Conway Institute, University College Dublin, Dublin, Ireland
| | - David R. Cotter
- Department of Psychiatry, Royal College of Surgeons in Ireland, Dublin, Ireland
| | - Philip McGuire
- Institute of Psychiatry, Psychology & Neuroscience, Department of Psychosis Studies, King’s College London, London, United Kingdom
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29
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Kirschner M, Schmidt A, Hodzic-Santor B, Burrer A, Manoliu A, Zeighami Y, Yau Y, Abbasi N, Maatz A, Habermeyer B, Abivardi A, Avram M, Brandl F, Sorg C, Homan P, Riecher-Rössler A, Borgwardt S, Seifritz E, Dagher A, Kaiser S. Orbitofrontal-Striatal Structural Alterations Linked to Negative Symptoms at Different Stages of the Schizophrenia Spectrum. Schizophr Bull 2020; 47:849-863. [PMID: 33257954 PMCID: PMC8084448 DOI: 10.1093/schbul/sbaa169] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 12/23/2022]
Abstract
Negative symptoms such as anhedonia and apathy are among the most debilitating manifestations of schizophrenia (SZ). Imaging studies have linked these symptoms to morphometric abnormalities in 2 brain regions implicated in reward and motivation: the orbitofrontal cortex (OFC) and striatum. Higher negative symptoms are generally associated with reduced OFC thickness, while higher apathy specifically maps to reduced striatal volume. However, it remains unclear whether these tissue losses are a consequence of chronic illness and its treatment or an underlying phenotypic trait. Here, we use multicentre magnetic resonance imaging data to investigate orbitofrontal-striatal abnormalities across the SZ spectrum from healthy populations with high schizotypy to unmedicated and medicated first-episode psychosis (FEP), and patients with chronic SZ. Putamen, caudate, accumbens volume, and OFC thickness were estimated from T1-weighted images acquired in all 3 diagnostic groups and controls from 4 sites (n = 337). Results were first established in 1 discovery dataset and replicated in 3 independent samples. There was a negative correlation between apathy and putamen/accumbens volume only in healthy individuals with schizotypy; however, medicated patients exhibited larger putamen volume, which appears to be a consequence of antipsychotic medications. The negative association between reduced OFC thickness and total negative symptoms also appeared to vary along the SZ spectrum, being significant only in FEP patients. In schizotypy, there was increased OFC thickness relative to controls. Our findings suggest that negative symptoms are associated with a temporal continuum of orbitofrontal-striatal abnormalities that may predate the occurrence of SZ. Thicker OFC in schizotypy may represent either compensatory or pathological mechanisms prior to the disease onset.
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Affiliation(s)
- Matthias Kirschner
- Montreal Neurological Institute, McGill University, Montreal, QC, Canada,Department of Psychiatry, Psychotherapy and Psychosomatics, Psychiatric Hospital, University of Zurich, Zurich, Switzerland,To whom correspondence should be addressed; 3801 Rue University, Montréal QC, H3A 2B4 Canada; tel: +1 514-398-1726, fax: +1 514–398–8948, e-mail:
| | - André Schmidt
- Department of Psychiatry (UPK), University of Basel, Basel, Switzerland
| | | | - Achim Burrer
- Department of Psychiatry, Psychotherapy and Psychosomatics, Psychiatric Hospital, University of Zurich, Zurich, Switzerland
| | - Andrei Manoliu
- Department of Psychiatry, Psychotherapy and Psychosomatics, Psychiatric Hospital, University of Zurich, Zurich, Switzerland,Wellcome Centre for Human Neuroimaging, University College London, London, UK,Max Planck University College London Centre for Computational Psychiatry and Ageing Research, London, UK
| | - Yashar Zeighami
- Montreal Neurological Institute, McGill University, Montreal, QC, Canada
| | - Yvonne Yau
- Montreal Neurological Institute, McGill University, Montreal, QC, Canada
| | - Nooshin Abbasi
- Montreal Neurological Institute, McGill University, Montreal, QC, Canada
| | - Anke Maatz
- Department of Psychiatry, Psychotherapy and Psychosomatics, Psychiatric Hospital, University of Zurich, Zurich, Switzerland
| | | | - Aslan Abivardi
- Department of Psychiatry, Psychotherapy and Psychosomatics, Psychiatric Hospital, University of Zurich, Zurich, Switzerland
| | - Mihai Avram
- Department of Neuroradiology and TUM-NIC Neuroimaging Center, Klinikum rechts der Isar, Technische Universität München, Munich, Germany,Department of Psychiatry, Psychosomatics and Psychotherapy, Schleswig Holstein University Hospital, University Lübeck, Lübeck Germany
| | - Felix Brandl
- Department of Psychiatry and TUM-NIC Neuroimaging Center, Klinikum rechts der Isar, Technische Universität München, Munich, Germany
| | - Christian Sorg
- Department of Neuroradiology and TUM-NIC Neuroimaging Center, Klinikum rechts der Isar, Technische Universität München, Munich, Germany,Department of Psychiatry and TUM-NIC Neuroimaging Center, Klinikum rechts der Isar, Technische Universität München, Munich, Germany
| | - Philipp Homan
- Department of Psychiatry, Psychotherapy and Psychosomatics, Psychiatric Hospital, University of Zurich, Zurich, Switzerland,Center for Psychiatric Neuroscience, Feinstein Institute for Medical Research, Manhasset, NY,Division of Psychiatry Research, Zucker Hillside Hospital, Northwell Health, New York, NY,Department of Psychiatry, Donald and Barbara Zucker School of Medicine at Northwell/Hofstra, Hempstead, NY
| | | | - Stefan Borgwardt
- Department of Psychiatry (UPK), University of Basel, Basel, Switzerland
| | - Erich Seifritz
- Department of Psychiatry, Psychotherapy and Psychosomatics, Psychiatric Hospital, University of Zurich, Zurich, Switzerland
| | - Alain Dagher
- Montreal Neurological Institute, McGill University, Montreal, QC, Canada
| | - Stefan Kaiser
- Department of Psychiatry, Division of Adult Psychiatry, Geneva University Hospitals, Geneva, Switzerland
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30
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Rollins CPE, Garrison JR, Arribas M, Seyedsalehi A, Li Z, Chan RCK, Yang J, Wang D, Liò P, Yan C, Yi ZH, Cachia A, Upthegrove R, Deakin B, Simons JS, Murray GK, Suckling J. Evidence in cortical folding patterns for prenatal predispositions to hallucinations in schizophrenia. Transl Psychiatry 2020; 10:387. [PMID: 33159044 PMCID: PMC7648757 DOI: 10.1038/s41398-020-01075-y] [Citation(s) in RCA: 14] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/14/2020] [Revised: 09/30/2020] [Accepted: 10/22/2020] [Indexed: 12/26/2022] Open
Abstract
All perception is a construction of the brain from sensory input. Our first perceptions begin during gestation, making fetal brain development fundamental to how we experience a diverse world. Hallucinations are percepts without origin in physical reality that occur in health and disease. Despite longstanding research on the brain structures supporting hallucinations and on perinatal contributions to the pathophysiology of schizophrenia, what links these two distinct lines of research remains unclear. Sulcal patterns derived from structural magnetic resonance (MR) images can provide a proxy in adulthood for early brain development. We studied two independent datasets of patients with schizophrenia who underwent clinical assessment and 3T MR imaging from the United Kingdom and Shanghai, China (n = 181 combined) and 63 healthy controls from Shanghai. Participants were stratified into those with (n = 79 UK; n = 22 Shanghai) and without (n = 43 UK; n = 37 Shanghai) hallucinations from the PANSS P3 scores for hallucinatory behaviour. We quantified the length, depth, and asymmetry indices of the paracingulate and superior temporal sulci (PCS, STS), which have previously been associated with hallucinations in schizophrenia, and constructed cortical folding covariance matrices organized by large-scale functional networks. In both ethnic groups, we demonstrated a significantly shorter left PCS in patients with hallucinations compared to those without, and to healthy controls. Reduced PCS length and STS depth corresponded to focal deviations in their geometry and to significantly increased covariance within and between areas of the salience and auditory networks. The discovery of neurodevelopmental alterations contributing to hallucinations establishes testable models for these enigmatic, sometimes highly distressing, perceptions and provides mechanistic insight into the pathological consequences of prenatal origins.
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Affiliation(s)
- Colleen P. E. Rollins
- grid.5335.00000000121885934Department of Psychiatry, University of Cambridge, Cambridge, UK
| | - Jane R. Garrison
- grid.5335.00000000121885934Department of Psychology, University of Cambridge, Cambridge, UK
| | - Maite Arribas
- grid.5335.00000000121885934Department of Physiology, Development and Neuroscience, University of Cambridge, Cambridge, UK ,grid.13097.3c0000 0001 2322 6764Institute of Psychiatry, Psychology, and Neuroscience, King’s College London, London, UK
| | - Aida Seyedsalehi
- grid.13097.3c0000 0001 2322 6764Institute of Psychiatry, Psychology, and Neuroscience, King’s College London, London, UK ,grid.450563.10000 0004 0412 9303Cambridgeshire and Peterborough NHS Foundation Trust, Cambridge, UK
| | - Zhi Li
- grid.9227.e0000000119573309Neuropsychology and Applied Cognitive Neuroscience Laboratory, CAS Key Laboratory of Mental Health, Institute of Psychology, Chinese Academy of Sciences, Beijing, China
| | - Raymond C. K. Chan
- grid.9227.e0000000119573309Neuropsychology and Applied Cognitive Neuroscience Laboratory, CAS Key Laboratory of Mental Health, Institute of Psychology, Chinese Academy of Sciences, Beijing, China
| | - Junwei Yang
- grid.5335.00000000121885934Department of Computer Science and Technology, University of Cambridge, Cambridge, UK
| | - Duo Wang
- grid.5335.00000000121885934Department of Computer Science and Technology, University of Cambridge, Cambridge, UK
| | - Pietro Liò
- grid.5335.00000000121885934Department of Computer Science and Technology, University of Cambridge, Cambridge, UK
| | - Chao Yan
- grid.22069.3f0000 0004 0369 6365Key Laboratory of Brain Functional Genomics (MOE & STCSM), School of Psychology and Cognitive Science, East China Normal University, Shanghai, China
| | - Zheng-hui Yi
- grid.16821.3c0000 0004 0368 8293Shanghai Mental Health Center, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Arnaud Cachia
- Université de Paris, LaPsyDÉ, CNRS, F-75005 Paris, France ,Université de Paris, IPNP, INSERM, F-75005 Paris, France
| | - Rachel Upthegrove
- grid.6572.60000 0004 1936 7486Institute for Mental Health, University of Birmingham, Birmingham, UK
| | - Bill Deakin
- grid.5379.80000000121662407Neuroscience and Psychiatry Unit, The University of Manchester, Manchester, UK
| | - Jon S. Simons
- grid.5335.00000000121885934Department of Psychology, University of Cambridge, Cambridge, UK
| | - Graham K. Murray
- grid.5335.00000000121885934Department of Psychiatry, University of Cambridge, Cambridge, UK ,grid.450563.10000 0004 0412 9303Cambridgeshire and Peterborough NHS Foundation Trust, Cambridge, UK
| | - John Suckling
- grid.5335.00000000121885934Department of Psychiatry, University of Cambridge, Cambridge, UK
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Individualized Diagnostic and Prognostic Models for Patients With Psychosis Risk Syndromes: A Meta-analytic View on the State of the Art. Biol Psychiatry 2020; 88:349-360. [PMID: 32305218 DOI: 10.1016/j.biopsych.2020.02.009] [Citation(s) in RCA: 50] [Impact Index Per Article: 12.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/30/2019] [Revised: 01/25/2020] [Accepted: 02/06/2020] [Indexed: 12/23/2022]
Abstract
BACKGROUND The clinical high risk (CHR) paradigm has facilitated research into the underpinnings of help-seeking individuals at risk for developing psychosis, aiming at predicting and possibly preventing transition to the overt disorder. Statistical methods such as machine learning and Cox regression have provided the methodological basis for this research by enabling the construction of diagnostic models (i.e., distinguishing CHR individuals from healthy individuals) and prognostic models (i.e., predicting a future outcome) based on different data modalities, including clinical, neurocognitive, and neurobiological data. However, their translation to clinical practice is still hindered by the high heterogeneity of both CHR populations and methodologies applied. METHODS We systematically reviewed the literature on diagnostic and prognostic models built on Cox regression and machine learning. Furthermore, we conducted a meta-analysis on prediction performances investigating heterogeneity of methodological approaches and data modality. RESULTS A total of 44 articles were included, covering 3707 individuals for prognostic studies and 1052 individuals for diagnostic studies (572 CHR patients and 480 healthy control subjects). CHR patients could be classified against healthy control subjects with 78% sensitivity and 77% specificity. Across prognostic models, sensitivity reached 67% and specificity reached 78%. Machine learning models outperformed those applying Cox regression by 10% sensitivity. There was a publication bias for prognostic studies yet no other moderator effects. CONCLUSIONS Our results may be driven by substantial clinical and methodological heterogeneity currently affecting several aspects of the CHR field and limiting the clinical implementability of the proposed models. We discuss conceptual and methodological harmonization strategies to facilitate more reliable and generalizable models for future clinical practice.
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Metzak PD, Devoe DJ, Iwaschuk A, Braun A, Addington J. Brain changes associated with negative symptoms in clinical high risk for psychosis: A systematic review. Neurosci Biobehav Rev 2020; 118:367-383. [PMID: 32768487 DOI: 10.1016/j.neubiorev.2020.07.041] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/17/2020] [Revised: 07/21/2020] [Accepted: 07/31/2020] [Indexed: 02/05/2023]
Abstract
The negative symptoms of schizophrenia are linked to poorer functional outcomes and decreases in quality of life, and are often the first to develop in individuals who are at clinical high risk (CHR) for psychosis. However, the accompanying neurobiological changes are poorly understood. Therefore, we conducted a systematic review of the studies that have examined the brain metrics associated with negative symptoms in those at CHR. Electronic databases were searched from inception to August 2019. Studies were selected if they mentioned negative symptoms in youth at CHR for psychosis, and brain imaging. Of 261 citations, 43 studies with 2144 CHR participants met inclusion criteria. Too few studies were focused on the same brain regions using similar neuroimaging methods to perform a meta-analysis, however, the results of this systematic review suggest a relationship between negative symptom increases and decreases in grey matter. The paucity of studies linking changes in brain structure and function with negative symptoms in those at CHR suggests that future work should focus on examining these relationships.
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Affiliation(s)
- Paul D Metzak
- Hotchkiss Brain Institute, Department of Psychiatry, Cumming School of Medicine, University of Calgary, 3330 Hospital Dr NW, Calgary, AB, T2N 4N1, Canada; Mathison Centre for Mental Health Research & Education, Hotchkiss Brain Institute, University of Calgary, 3280 Hospital Dr NW, Calgary, AB, T2N 4Z6, Canada.
| | - Daniel J Devoe
- Hotchkiss Brain Institute, Department of Psychiatry, Cumming School of Medicine, University of Calgary, 3330 Hospital Dr NW, Calgary, AB, T2N 4N1, Canada; Mathison Centre for Mental Health Research & Education, Hotchkiss Brain Institute, University of Calgary, 3280 Hospital Dr NW, Calgary, AB, T2N 4Z6, Canada.
| | - Amanda Iwaschuk
- Hotchkiss Brain Institute, Department of Psychiatry, Cumming School of Medicine, University of Calgary, 3330 Hospital Dr NW, Calgary, AB, T2N 4N1, Canada; Mathison Centre for Mental Health Research & Education, Hotchkiss Brain Institute, University of Calgary, 3280 Hospital Dr NW, Calgary, AB, T2N 4Z6, Canada.
| | - Amy Braun
- Hotchkiss Brain Institute, Department of Psychiatry, Cumming School of Medicine, University of Calgary, 3330 Hospital Dr NW, Calgary, AB, T2N 4N1, Canada; Mathison Centre for Mental Health Research & Education, Hotchkiss Brain Institute, University of Calgary, 3280 Hospital Dr NW, Calgary, AB, T2N 4Z6, Canada.
| | - Jean Addington
- Hotchkiss Brain Institute, Department of Psychiatry, Cumming School of Medicine, University of Calgary, 3330 Hospital Dr NW, Calgary, AB, T2N 4N1, Canada; Mathison Centre for Mental Health Research & Education, Hotchkiss Brain Institute, University of Calgary, 3280 Hospital Dr NW, Calgary, AB, T2N 4Z6, Canada.
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MacKinley ML, Sabesan P, Palaniyappan L. Deviant cortical sulcation related to schizophrenia and cognitive deficits in the second trimester. Transl Neurosci 2020; 11:236-240. [PMID: 33312722 PMCID: PMC7705986 DOI: 10.1515/tnsci-2020-0111] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/10/2020] [Revised: 06/05/2020] [Accepted: 06/16/2020] [Indexed: 12/14/2022] Open
Abstract
Objectives Aberrant cortical development, inferred from cortical folding, is linked to the risk of schizophrenia. Cortical folds develop in a time-locked fashion during fetal growth. We leveraged this temporal specificity of sulcation to investigate the timing of the prenatal insult linked to schizophrenia and the cognitive impairment seen in this illness. Methods Anatomical MRI scans from 68 patients with schizophrenia and 72 controls were used to evaluate the sulcal depth of five major invariable primary sulci representing lobar development (calcarine sulcus, superior temporal sulcus, superior frontal sulcus, intraparietal sulcus and inferior frontal sulcus) with formation representing the distinct developmental periods. Results A repeated-measure ANOVA with five sulci and two hemispheres as the within-subject factors and gender, age and intracranial volume as covariates revealed a significant effect of diagnosis (F[1,134] = 14.8, p = 0.0002). Control subjects had deeper bilateral superior temporal, right inferior frontal and left calcarine sulci. A deeper superior frontal sulcus predicted better cognitive scores among patients. Conclusion Our results suggest that the gestational disruption underlying schizophrenia is likely to predate, if not coincide with the appearance of calcarine sulcus (early second trimester). Nevertheless, the burden of cognitive deficits may relate specifically to the aberrant superior frontal development apparent in late second trimester.
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Affiliation(s)
- Michael Lloyd MacKinley
- Robarts Research Institute & The Brain and Mind Institute, University of Western Ontario, Room-A2/636, Prevention and Early Intervention Program for Psychoses, Victoria Hospital, 800, Commissioners Road, London, Ontario, Canada.,Schulich School of Medicine and Dentistry, Department of Neuroscience, University of Western Ontario, London, Ontario, Canada
| | - Priyadharshini Sabesan
- Department of Psychiatry, University of Western Ontario, London, Ontario, Canada.,Lawson Health Research Institute, London, Ontario, Canada
| | - Lena Palaniyappan
- Robarts Research Institute & The Brain and Mind Institute, University of Western Ontario, Room-A2/636, Prevention and Early Intervention Program for Psychoses, Victoria Hospital, 800, Commissioners Road, London, Ontario, Canada.,Department of Psychiatry, University of Western Ontario, London, Ontario, Canada.,Lawson Health Research Institute, London, Ontario, Canada
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Yang J, Pu W, Wu G, Chen E, Lee E, Liu Z, Palaniyappan L. Connectomic Underpinnings of Working Memory Deficits in Schizophrenia: Evidence From a replication fMRI study. Schizophr Bull 2020; 46:916-926. [PMID: 32016430 PMCID: PMC7345823 DOI: 10.1093/schbul/sbz137] [Citation(s) in RCA: 23] [Impact Index Per Article: 5.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 12/11/2022]
Abstract
BACKGROUND Working memory (WM) deficit is a key feature of schizophrenia that relates to a generalized neural inefficiency of extensive brain areas. To date, it remains unknown how these distributed regions are systemically organized at the connectome level and how the disruption of such organization brings about the WM impairment seen in schizophrenia. METHODS We used graph theory to examine the neural efficiency of the functional connectome in different granularity in 155 patients with schizophrenia and 96 healthy controls during a WM task. These analyses were repeated in another independent dataset (81 patients and 54 controls). Linear regression analysis was used to test associations of altered graph properties, clinical symptoms, and WM accuracy in patients. A machine-learning approach was adopted to study the ability of multivariate connectome features from one dataset to discriminate patients from controls in the second dataset. RESULTS Small-worldness of the whole-brain connectome was significantly increased in schizophrenia during the WM task; this increase is related to better (though subpar) WM accuracy in patients with more severe negative symptom burden. There was a shift in the degree distribution to a more homogeneous form in patients. The machine-learning approach classified a new set of patients from controls with 84.3% true-positivity rate for schizophrenia and 71.6% overall accuracy. CONCLUSIONS We demonstrate a putative mechanistic link between connectome topology, hub redistribution, and impaired n-back performance in schizophrenia. The task-dependent modulation of the connectome relates to, but remains inefficient in, improving the performance above par in the presence of severe negative symptoms.
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Affiliation(s)
- Jie Yang
- Institute of Mental Health, Second Xiangya Hospital, Central South University, Changsha, PR China
| | - Weidan Pu
- Medical Psychological Center, the Second Xiangya Hospital, Central South University, Changsha, P.R. China
- Medical Psychological Institute of Central South University, Changsha, P.R. China
| | - Guowei Wu
- Institute of Mental Health, Second Xiangya Hospital, Central South University, Changsha, PR China
| | - Eric Chen
- Department of Psychiatry, The University of Hong Kong, Pok Fu Lam, Hong Kong
| | - Edwin Lee
- Department of Psychiatry, The University of Hong Kong, Pok Fu Lam, Hong Kong
| | - Zhening Liu
- Institute of Mental Health, Second Xiangya Hospital, Central South University, Changsha, PR China
| | - Lena Palaniyappan
- Institute of Mental Health, Second Xiangya Hospital, Central South University, Changsha, PR China
- Department of Psychiatry, University of Western Ontario, London, ON, Canada
- Robarts Research Institute, University of Western Ontario, London, ON, Canada
- Lawson Health Research Institute, London, ON, Canada
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35
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Tognin S, van Hell HH, Merritt K, Winter-van Rossum I, Bossong MG, Kempton MJ, Modinos G, Fusar-Poli P, Mechelli A, Dazzan P, Maat A, de Haan L, Crespo-Facorro B, Glenthøj B, Lawrie SM, McDonald C, Gruber O, van Amelsvoort T, Arango C, Kircher T, Nelson B, Galderisi S, Bressan R, Kwon JS, Weiser M, Mizrahi R, Sachs G, Maatz A, Kahn R, McGuire P. Towards Precision Medicine in Psychosis: Benefits and Challenges of Multimodal Multicenter Studies-PSYSCAN: Translating Neuroimaging Findings From Research into Clinical Practice. Schizophr Bull 2020; 46:432-441. [PMID: 31424555 PMCID: PMC7043057 DOI: 10.1093/schbul/sbz067] [Citation(s) in RCA: 49] [Impact Index Per Article: 12.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 01/28/2023]
Abstract
In the last 2 decades, several neuroimaging studies investigated brain abnormalities associated with the early stages of psychosis in the hope that these could aid the prediction of onset and clinical outcome. Despite advancements in the field, neuroimaging has yet to deliver. This is in part explained by the use of univariate analytical techniques, small samples and lack of statistical power, lack of external validation of potential biomarkers, and lack of integration of nonimaging measures (eg, genetic, clinical, cognitive data). PSYSCAN is an international, longitudinal, multicenter study on the early stages of psychosis which uses machine learning techniques to analyze imaging, clinical, cognitive, and biological data with the aim of facilitating the prediction of psychosis onset and outcome. In this article, we provide an overview of the PSYSCAN protocol and we discuss benefits and methodological challenges of large multicenter studies that employ neuroimaging measures.
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Affiliation(s)
- Stefania Tognin
- Department of Psychosis Studies, Institute of Psychiatry, Psychology and Neuroscience, King’s College London, London, UK,Outreach and Support in South London (OASIS), South London and Maudsley NHS Foundation Trust, London, UK
| | - Hendrika H van Hell
- Department of Psychiatry, University Medical Center Utrecht Brain Center, Utrecht, the Netherlands,To whom correspondence should be addressed; Clinical Trial Center, Department of Psychiatry, University Medical Center Utrecht Brain Center, PO Box 85500, 3508 GA Utrecht, The Netherlands; tel: +31 88 755 7247, e-mail:
| | - Kate Merritt
- Department of Psychosis Studies, Institute of Psychiatry, Psychology and Neuroscience, King’s College London, London, UK
| | - Inge Winter-van Rossum
- Department of Psychiatry, University Medical Center Utrecht Brain Center, Utrecht, the Netherlands
| | - Matthijs G Bossong
- Department of Psychiatry, University Medical Center Utrecht Brain Center, Utrecht, the Netherlands
| | - Matthew J Kempton
- Department of Psychosis Studies, Institute of Psychiatry, Psychology and Neuroscience, King’s College London, London, UK
| | - Gemma Modinos
- Department of Psychosis Studies, Institute of Psychiatry, Psychology and Neuroscience, King’s College London, London, UK
| | - Paolo Fusar-Poli
- Department of Psychosis Studies, Institute of Psychiatry, Psychology and Neuroscience, King’s College London, London, UK,Department of Brain and Behavioral Sciences, University of Pavia, Pavia, Italy,National Institute for Health Research, Maudsley Biomedical Research Centre, South London and Maudsley NHS Foundation Trust, London, UK
| | - Andrea Mechelli
- Department of Psychosis Studies, Institute of Psychiatry, Psychology and Neuroscience, King’s College London, London, UK
| | - Paola Dazzan
- Department of Psychosis Studies, Institute of Psychiatry, Psychology and Neuroscience, King’s College London, London, UK
| | - Arija Maat
- Department of Psychiatry, University Medical Center Utrecht Brain Center, Utrecht, the Netherlands
| | - Lieuwe de Haan
- Department Early Psychosis, Psychiatry, Amsterdam UMC, University of Amsterdam, Amsterdam, The Netherlands
| | - Benedicto Crespo-Facorro
- CIBERSAM, Department of Psychiatry, University Hospital Virgen del Rocío, Sevilla, Spain,IDIVAL, Marqués de Valdecilla University Hospital, Santander, Spain,School of Medicine, University of Cantabria, Santander, Spain
| | - Birte Glenthøj
- Centre for Neuropsychiatric Schizophrenia Research (CNSR), Mental Health Centre Glostrup, University of Copenhagen, Glostrup, Denmark,Centre for Clinical Intervention and Neuropsychiatric Schizophrenia Research (CINS), Mental Health Centre Glostrup, University of Copenhagen, Glostrup, Denmark
| | - Stephen M Lawrie
- Division of Psychiatry, University of Edinburgh, Royal Edinburgh Hospital, Edinburgh, UK
| | - Colm McDonald
- Centre for Neuroimaging & Cognitive Genomics (NICOG), NCBES Galway Neuroscience Centre, National University of Ireland Galway, Galway, Ireland
| | - Oliver Gruber
- Section for Experimental Psychopathology and Neuroimaging, Department of General Psychiatry, Heidelberg University, Heidelberg, Germany
| | - Therese van Amelsvoort
- Department of Psychiatry and Neuropsychology, Maastricht University, Maastricht, The Netherlands
| | - Celso Arango
- Department of Child and Adolescent Psychiatry, Institute of Psychiatry and Mental Health, Hospital General Universitario Gregorio Marañon, CIBERSAM, School of Medicine, Universidad Complutense, Madrid, Spain
| | - Tilo Kircher
- Department of Psychiatry, University of Marburg, Marburg, Germany
| | - Barnaby Nelson
- Orygen, The National Centre of Excellence in Youth Mental Health, Melbourne, Australia,Centre for Youth Mental Health, The University of Melbourne, Melbourne, Australia
| | - Silvana Galderisi
- Department of Psychiatry, University of Campania Luigi Vanvitelli, Naples, Italy
| | - Rodrigo Bressan
- Interdisciplinary Lab for Clinical Neurosciences (LiNC), Department of Psychiatry, Universidade Federal de Sao Paulo (UNIFESP), Sao Paulo, Brazil
| | - Jun S Kwon
- Department of Psychiatry, Seoul National University College of Medicine, Seoul, Korea
| | - Mark Weiser
- Department of Psychiatry, Sheba Medical Center, Tel Hashomer, Israel,Sackler School of Medicine, Tel Aviv University, Tel Aviv, Israel
| | - Romina Mizrahi
- Institute of Medical Science, University of Toronto, Toronto, Canada,Centre for Addiction and Mental Health, Toronto, Canada,Department of Psychiatry, University of Toronto, Toronto, Canada
| | - Gabriele Sachs
- Department of Psychiatry and Psychotherapy, Medical University of Vienna, Vienna, Austria
| | - Anke Maatz
- Department of Psychiatry, Psychotherapy and Psychosomatics, Psychiatric Hospital, University of Zurich, Zurich, Switzerland
| | - René Kahn
- Department of Psychiatry, University Medical Center Utrecht Brain Center, Utrecht, the Netherlands,Department of Psychiatry, Icahn School of Medicine at Mount Sinai, New York, NY
| | - Phillip McGuire
- Department of Psychosis Studies, Institute of Psychiatry, Psychology and Neuroscience, King’s College London, London, UK,Outreach and Support in South London (OASIS), South London and Maudsley NHS Foundation Trust, London, UK,National Institute for Health Research, Maudsley Biomedical Research Centre, South London and Maudsley NHS Foundation Trust, London, UK
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36
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Schmidt A, Borgwardt S. Implementing MR Imaging into Clinical Routine Screening in Patients with Psychosis? Neuroimaging Clin N Am 2020; 30:65-72. [DOI: 10.1016/j.nic.2019.09.004] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/06/2023]
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37
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Yun JY, Boedhoe PSW, Vriend C, Jahanshad N, Abe Y, Ameis SH, Anticevic A, Arnold PD, Batistuzzo MC, Benedetti F, Beucke JC, Bollettini I, Bose A, Brem S, Calvo A, Cheng Y, Cho KIK, Ciullo V, Dallaspezia S, Denys D, Feusner JD, Fouche JP, Giménez M, Gruner P, Hibar DP, Hoexter MQ, Hu H, Huyser C, Ikari K, Kathmann N, Kaufmann C, Koch K, Lazaro L, Lochner C, Marques P, Marsh R, Martínez-Zalacaín I, Mataix-Cols D, Menchón JM, Minuzzi L, Morgado P, Moreira P, Nakamae T, Nakao T, Narayanaswamy JC, Nurmi EL, O'Neill J, Piacentini J, Piras F, Piras F, Reddy YCJ, Sato JR, Simpson HB, Soreni N, Soriano-Mas C, Spalletta G, Stevens MC, Szeszko PR, Tolin DF, Venkatasubramanian G, Walitza S, Wang Z, van Wingen GA, Xu J, Xu X, Zhao Q, Thompson PM, Stein DJ, van den Heuvel OA, Kwon JS. Brain structural covariance networks in obsessive-compulsive disorder: a graph analysis from the ENIGMA Consortium. Brain 2020; 143:684-700. [PMID: 32040561 PMCID: PMC7009583 DOI: 10.1093/brain/awaa001] [Citation(s) in RCA: 40] [Impact Index Per Article: 10.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/11/2019] [Revised: 11/24/2019] [Accepted: 11/26/2019] [Indexed: 12/13/2022] Open
Abstract
Brain structural covariance networks reflect covariation in morphology of different brain areas and are thought to reflect common trajectories in brain development and maturation. Large-scale investigation of structural covariance networks in obsessive-compulsive disorder (OCD) may provide clues to the pathophysiology of this neurodevelopmental disorder. Using T1-weighted MRI scans acquired from 1616 individuals with OCD and 1463 healthy controls across 37 datasets participating in the ENIGMA-OCD Working Group, we calculated intra-individual brain structural covariance networks (using the bilaterally-averaged values of 33 cortical surface areas, 33 cortical thickness values, and six subcortical volumes), in which edge weights were proportional to the similarity between two brain morphological features in terms of deviation from healthy controls (i.e. z-score transformed). Global networks were characterized using measures of network segregation (clustering and modularity), network integration (global efficiency), and their balance (small-worldness), and their community membership was assessed. Hub profiling of regional networks was undertaken using measures of betweenness, closeness, and eigenvector centrality. Individually calculated network measures were integrated across the 37 datasets using a meta-analytical approach. These network measures were summated across the network density range of K = 0.10-0.25 per participant, and were integrated across the 37 datasets using a meta-analytical approach. Compared with healthy controls, at a global level, the structural covariance networks of OCD showed lower clustering (P < 0.0001), lower modularity (P < 0.0001), and lower small-worldness (P = 0.017). Detection of community membership emphasized lower network segregation in OCD compared to healthy controls. At the regional level, there were lower (rank-transformed) centrality values in OCD for volume of caudate nucleus and thalamus, and surface area of paracentral cortex, indicative of altered distribution of brain hubs. Centrality of cingulate and orbito-frontal as well as other brain areas was associated with OCD illness duration, suggesting greater involvement of these brain areas with illness chronicity. In summary, the findings of this study, the largest brain structural covariance study of OCD to date, point to a less segregated organization of structural covariance networks in OCD, and reorganization of brain hubs. The segregation findings suggest a possible signature of altered brain morphometry in OCD, while the hub findings point to OCD-related alterations in trajectories of brain development and maturation, particularly in cingulate and orbitofrontal regions.
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Affiliation(s)
- Je-Yeon Yun
- Seoul National University Hospital, Seoul, Republic of Korea
- Yeongeon Student Support Center, Seoul National University College of Medicine, Seoul, Republic of Korea
| | - Premika S W Boedhoe
- Amsterdam UMC, Vrije Universiteit Amsterdam, Department of Psychiatry, Amsterdam Neuroscience, Amsterdam, The Netherlands
- Amsterdam UMC, Vrije Universiteit Amsterdam, Department of Anatomy & Neurosciences, Amsterdam Neuroscience, Amsterdam, The Netherlands
| | - Chris Vriend
- Amsterdam UMC, Vrije Universiteit Amsterdam, Department of Psychiatry, Amsterdam Neuroscience, Amsterdam, The Netherlands
- Amsterdam UMC, Vrije Universiteit Amsterdam, Department of Anatomy & Neurosciences, Amsterdam Neuroscience, Amsterdam, The Netherlands
| | - Neda Jahanshad
- Imaging Genetics Center, Mark and Mary Stevens Neuroimaging & Informatics Institute, Keck School of Medicine of the University of Southern California, Marina del Rey, CA, USA
| | - Yoshinari Abe
- Department of Psychiatry, Graduate School of Medical Science, Kyoto Prefectural University of Medicine, Kyoto, Japan
| | - Stephanie H Ameis
- The Margaret and Wallace McCain Centre for Child, Youth & Family Mental Health, Campbell Family Mental Health Research Institute, The Centre for Addiction and Mental Health, Department of Psychiatry, Faculty of Medicine, University of Toronto, Toronto, Canada
- Centre for Brain and Mental Health, The Hospital for Sick Children, Toronto, Canada
| | - Alan Anticevic
- Department of Psychiatry, Yale University School of Medicine, New Haven, Connecticut, USA
| | - Paul D Arnold
- Mathison Centre for Mental Health Research and Education, Hotchkiss Brain Institute, Cumming School of Medicine, University of Calgary, Calgary, Alberta, Canada
- Department of Psychiatry, Cumming School of Medicine, University of Calgary, Calgary, Alberta, Canada
| | - Marcelo C Batistuzzo
- Departamento e Instituto de Psiquiatria do Hospital das Clinicas, IPQ HCFMUSP, Faculdade de Medicina, Universidade de Sao Paulo, SP, Brazil
| | - Francesco Benedetti
- Psychiatry and Clinical Psychobiology, Division of Neuroscience, Scientific Institute Ospedale San Raffaele, Milano, Italy
| | - Jan C Beucke
- Department of Psychology, Humboldt-Universität zu Berlin, Berlin, Germany
| | - Irene Bollettini
- Psychiatry and Clinical Psychobiology, Division of Neuroscience, Scientific Institute Ospedale San Raffaele, Milano, Italy
| | - Anushree Bose
- Obsessive-Compulsive Disorder (OCD) Clinic Department of Psychiatry National Institute of Mental Health and Neurosciences, Bangalore, India
| | - Silvia Brem
- Department of Child and Adolescent Psychiatry and Psychotherapy, Psychiatric Hospital, University of Zurich, Zurich, Switzerland
| | - Anna Calvo
- Magnetic Resonance Image Core Facility, IDIBAPS (Institut d'Investigacions Biomèdiques August Pi i Sunyer), Barcelona, Spain
| | - Yuqi Cheng
- Department of Psychiatry, First Affiliated Hospital of Kunming Medical University, Kunming, China
| | - Kang Ik K Cho
- Institute of Human Behavioral Medicine, SNU-MRC, Seoul, Republic of Korea
| | - Valentina Ciullo
- Laboratory of Neuropsychiatry, Department of Clinical and Behavioral Neurology, IRCCS Santa Lucia Foundation, Rome, Italy
| | - Sara Dallaspezia
- Psychiatry and Clinical Psychobiology, Division of Neuroscience, Scientific Institute Ospedale San Raffaele, Milano, Italy
| | - Damiaan Denys
- Amsterdam UMC, University of Amsterdam, Department of Psychiatry, Amsterdam Neuroscience, Amsterdam, The Netherlands
- Netherlands Institute for Neuroscience, Royal Netherlands Academy of Arts and Sciences, Amsterdam, The Netherlands
| | - Jamie D Feusner
- Semel Institute for Neuroscience and Human Behavior, University of California, Los Angeles, CA, USA
| | - Jean-Paul Fouche
- SAMRC Unit on Risk and Resilience in Mental Disorders, Department of Psychiatry, University of Cape Town, Cape Town, South Africa
| | - Mònica Giménez
- Centro de Investigación Biomédica en Red de Salud Mental (CIBERSAM), Carlos III Health Institute, Barcelona, Spain
- Department of Psychiatry, Bellvitge University Hospital, Bellvitge Biomedical Research Institute-IDIBELL, L'Hospitalet de Llobregat, Barcelona, Spain
| | - Patricia Gruner
- Department of Psychiatry, Yale University School of Medicine, New Haven, Connecticut, USA
| | - Derrek P Hibar
- Imaging Genetics Center, Mark and Mary Stevens Neuroimaging & Informatics Institute, Keck School of Medicine of the University of Southern California, Marina del Rey, CA, USA
| | - Marcelo Q Hoexter
- Departamento e Instituto de Psiquiatria do Hospital das Clinicas, IPQ HCFMUSP, Faculdade de Medicina, Universidade de Sao Paulo, SP, Brazil
| | - Hao Hu
- Shanghai Mental Health Center Shanghai Jiao Tong University School of Medicine, PR China
| | - Chaim Huyser
- De Bascule, Academic Center for Child and Adolescent Psychiatry, Amsterdam, The Netherlands
- Amsterdam UMC, Department of Child and Adolescent Psychiatry, Amsterdam, The Netherlands
| | - Keisuke Ikari
- Department of Neuropsychiatry, Graduate School of Medical Sciences, Kyushu University, 3-1-1 Maidashi Higashi-ku, Fukuoka, Japan
| | - Norbert Kathmann
- Department of Psychology, Humboldt-Universität zu Berlin, Berlin, Germany
| | - Christian Kaufmann
- Department of Psychology, Humboldt-Universität zu Berlin, Berlin, Germany
| | - Kathrin Koch
- Department of Neuroradiology, Klinikum rechts der Isar, Technische Universität München, Germany
- TUM-Neuroimaging Center (TUM-NIC) of Klinikum rechts der Isar, Technische Universität München, Germany
| | - Luisa Lazaro
- Department of Child and Adolescent Psychiatry and Psychology, Institute of Neurosciences, Hospital Clínic Universitari, Barcelona, Spain
- Institut d'Investigacions Biomèdiques August Pi i Sunyer (IDIBAPS), Barcelona, Spain
- Department of Medicine, University of Barcelona, Barcelona, Spain
- Centro de Investigación Biomèdica en Red de Salud Mental (CIBERSAM), Barcelona, Spain
| | - Christine Lochner
- SAMRC Unit on Anxiety and Stress Disorders, Department of Psychiatry, University of Stellenbosch, South Africa
| | - Paulo Marques
- Life and Health Sciences Research Institute (ICVS), School of Medicine, University of Minho, Braga, Portugal
| | - Rachel Marsh
- Columbia University Medical College, Columbia University, New York, NY, USA
- The New York State Psychiatric Institute, New York, NY, USA
| | - Ignacio Martínez-Zalacaín
- Department of Psychiatry, Bellvitge University Hospital, Bellvitge Biomedical Research Institute-IDIBELL, L'Hospitalet de Llobregat, Barcelona, Spain
- Department of Clinical Sciences, University of Barcelona, Spain
| | - David Mataix-Cols
- Department of Clinical Neuroscience, Centre for Psychiatry Research, Karolinska Institutet, Stockholm, Sweden
| | - José M Menchón
- Department of Psychiatry, Bellvitge University Hospital, Bellvitge Biomedical Research Institute-IDIBELL, L'Hospitalet de Llobregat, Barcelona, Spain
- Centro de Investigación Biomèdica en Red de Salud Mental (CIBERSAM), Barcelona, Spain
- Department of Clinical Sciences, University of Barcelona, Spain
| | - Luciano Minuzzi
- McMaster University, Department of Psychiatry and Behavioural Neurosciences, Hamilton, Ontario, Canada
| | - Pedro Morgado
- Life and Health Sciences Research Institute (ICVS), School of Medicine, University of Minho, Braga, Portugal
- Clinical Academic Center-Braga, Braga, Portugal
- ICVS-3Bs PT Government Associate Laboratory, Braga, Portugal
| | - Pedro Moreira
- Life and Health Sciences Research Institute (ICVS), School of Medicine, University of Minho, Braga, Portugal
- Clinical Academic Center-Braga, Braga, Portugal
- ICVS-3Bs PT Government Associate Laboratory, Braga, Portugal
| | - Takashi Nakamae
- Department of Psychiatry, Graduate School of Medical Science, Kyoto Prefectural University of Medicine, Kyoto, Japan
| | - Tomohiro Nakao
- Department of Neuropsychiatry, Graduate School of Medical Sciences, Kyushu University, Fukuoka, Japan
| | - Janardhanan C Narayanaswamy
- Obsessive-Compulsive Disorder (OCD) Clinic Department of Psychiatry National Institute of Mental Health and Neurosciences, Bangalore, India
| | - Erika L Nurmi
- Semel Institute for Neuroscience and Human Behavior, University of California, Los Angeles, CA, USA
| | - Joseph O'Neill
- Semel Institute for Neuroscience and Human Behavior, University of California, Los Angeles, CA, USA
- Division of Child and Adolescent Psychiatry, University of California, Los Angeles, CA, USA
| | - John Piacentini
- Semel Institute for Neuroscience and Human Behavior, University of California, Los Angeles, CA, USA
- Division of Child and Adolescent Psychiatry, University of California, Los Angeles, CA, USA
| | - Fabrizio Piras
- Laboratory of Neuropsychiatry, Department of Clinical and Behavioral Neurology, IRCCS Santa Lucia Foundation, Rome, Italy
| | - Federica Piras
- Laboratory of Neuropsychiatry, Department of Clinical and Behavioral Neurology, IRCCS Santa Lucia Foundation, Rome, Italy
| | - Y C Janardhan Reddy
- Obsessive-Compulsive Disorder (OCD) Clinic Department of Psychiatry National Institute of Mental Health and Neurosciences, Bangalore, India
| | - Joao R Sato
- Center for Mathematics, Computing and Cognition, Universidade Federal do ABC, Santo Andre, Brazil
| | - H Blair Simpson
- Columbia University Medical College, Columbia University, New York, NY, USA
- Center for OCD and Related Disorders, New York State Psychiatric Institute, New York, NY, USA
| | - Noam Soreni
- Pediatric OCD Consultation Service, Anxiety Treatment and Research Center, St. Joseph's HealthCare, Hamilton, Ontario, Canada
| | - Carles Soriano-Mas
- Department of Psychiatry, Bellvitge University Hospital, Bellvitge Biomedical Research Institute-IDIBELL, L'Hospitalet de Llobregat, Barcelona, Spain
- Centro de Investigación Biomèdica en Red de Salud Mental (CIBERSAM), Barcelona, Spain
- Department of Psychobiology and Methodology of Health Sciences, Universitat Autònoma de Barcelona, Spain
| | - Gianfranco Spalletta
- Laboratory of Neuropsychiatry, Department of Clinical and Behavioral Neurology, IRCCS Santa Lucia Foundation, Rome, Italy
- Beth K. and Stuart C. Yudofsky Division of Neuropsychiatry, Department of Psychiatry and Behavioral Sciences, Baylor College of Medicine, Houston, Texas, USA
| | - Michael C Stevens
- Yale University School of Medicine, New Haven, Connecticut, USA
- Clinical Neuroscience and Development Laboratory, Olin Neuropsychiatry Research Center, Hartford, Connecticut, USA
| | - Philip R Szeszko
- Icahn School of Medicine at Mount Sinai, New York, USA
- James J. Peters VA Medical Center, Bronx, New York, USA
| | - David F Tolin
- Yale University School of Medicine, New Haven, Connecticut, USA
- Institute of Living/Hartford Hospital, Hartford, Connecticut, USA
| | - Ganesan Venkatasubramanian
- Obsessive-Compulsive Disorder (OCD) Clinic Department of Psychiatry National Institute of Mental Health and Neurosciences, Bangalore, India
| | - Susanne Walitza
- Department of Child and Adolescent Psychiatry and Psychotherapy, Psychiatric Hospital, University of Zurich, Zurich, Switzerland
| | - Zhen Wang
- Shanghai Mental Health Center Shanghai Jiao Tong University School of Medicine, PR China
- Shanghai Key Laboratory of Psychotic Disorders, PR China
| | - Guido A van Wingen
- Amsterdam UMC, University of Amsterdam, Department of Psychiatry, Amsterdam Neuroscience, Amsterdam, The Netherlands
| | - Jian Xu
- Department of Psychiatry, The First Affiliated Hospital of Kunming Medical University, Kunming, Yunnan, PR China
| | - Xiufeng Xu
- Department of Psychiatry, The First Affiliated Hospital of Kunming Medical University, Kunming, Yunnan, PR China
| | - Qing Zhao
- Shanghai Mental Health Center Shanghai Jiao Tong University School of Medicine, PR China
| | - Paul M Thompson
- Imaging Genetics Center, Mark and Mary Stevens Neuroimaging & Informatics Institute, Keck School of Medicine of the University of Southern California, Marina del Rey, CA, USA
| | - Dan J Stein
- SAMRC Unit on Risk and Resilience in Mental Disorders, Department of Psychiatry, University of Cape Town, Cape Town, South Africa
| | - Odile A van den Heuvel
- Amsterdam UMC, Vrije Universiteit Amsterdam, Department of Psychiatry, Amsterdam Neuroscience, Amsterdam, The Netherlands
- Amsterdam UMC, Vrije Universiteit Amsterdam, Department of Anatomy & Neurosciences, Amsterdam Neuroscience, Amsterdam, The Netherlands
| | - Jun Soo Kwon
- Department of Psychiatry, Seoul National University College of Medicine, Seoul, Republic of Korea
- Department of Brain and Cognitive Sciences, Seoul National University College of Natural Sciences, Seoul, Republic of Korea
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38
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Yun JY, Kim YK. Phenotype Network and Brain Structural Covariance Network of Anxiety. ADVANCES IN EXPERIMENTAL MEDICINE AND BIOLOGY 2020; 1191:21-34. [PMID: 32002920 DOI: 10.1007/978-981-32-9705-0_2] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/23/2022]
Abstract
Network-based approach for psychological phenotypes assumes the dynamical interactions among the psychiatric symptoms, psychological characteristics, and neurocognitive performances arise, as they coexist, propagate, and inhibit other components within the network of mental phenomena. For differential types of dataset from which the phenotype network is to be estimated, a Gaussian graphical model, an Ising model, a directed acyclic graph, or an intraindividual covariance network could be used. Accordingly, these network-based approaches for anxiety-related psychological phenomena have been helpful in quantitative and pictorial understanding of qualitative dynamics among the diverse psychological phenomena as well as mind-environment interactions. Brain structural covariance refers to the correlative patterns of diverse brain morphological features among differential brain regions comprising the brain, as calculated per participant or across the participants. These covarying patterns of brain morphology partly overlap with longitudinal patterns of brain cortical maturation and also with propagating pattern of brain morphological changes such as cortical thinning and brain volume reduction in patients diagnosed with neurologic or psychiatric disorders along the trajectory of disease progression. Previous studies that used the brain structural covariance network could show neural correlates of specific anxiety disorder such as panic disorder and also elucidate the neural underpinning of anxiety symptom severity in diverse psychiatric and neurologic disorder patients.
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Affiliation(s)
- Je-Yeon Yun
- Seoul National University Hospital, Seoul, South Korea. .,Yeongeon Student Support Center, Seoul National University College of Medicine, Seoul, South Korea.
| | - Yong-Ku Kim
- Department of Psychiatry, College of Medicine, Korea University, Seoul, South Korea
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Sasabayashi D, Takayanagi Y, Takahashi T, Nemoto K, Furuichi A, Kido M, Nishikawa Y, Nakamura M, Noguchi K, Suzuki M. Increased brain gyrification in the schizophrenia spectrum. Psychiatry Clin Neurosci 2020; 74:70-76. [PMID: 31596011 DOI: 10.1111/pcn.12939] [Citation(s) in RCA: 24] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/20/2019] [Accepted: 09/24/2019] [Indexed: 12/22/2022]
Abstract
AIM Increased brain gyrification in diverse cortical regions has been reported in patients with schizophrenia, possibly reflecting deviations in early neurodevelopment. However, it remains unknown whether patients with schizotypal disorder exhibit similar changes. METHODS This magnetic resonance imaging study investigated brain gyrification in 46 patients with schizotypal disorder (29 male, 17 female), 101 patients with schizophrenia (55 male, 46 female), and 77 healthy controls (44 male, 33 female). T1-weighted magnetic resonance images were obtained for each participant. Using FreeSurfer software, the local gyrification index (LGI) of the entire cortex was compared across the groups. RESULTS Both schizophrenia and schizotypal disorder patients showed a significantly higher LGI in diverse cortical regions, including the bilateral prefrontal and left parietal cortices, as compared with controls, but its extent was broader in schizophrenia especially for the right prefrontal and left occipital regions. No significant correlations were found between the LGI and clinical variables (e.g., symptom severity, medication) for either of the patient groups. CONCLUSION Increased LGI in the frontoparietal regions was common to both patient groups and might represent vulnerability to schizophrenia, while more diverse changes in schizophrenia patients might be associated with the manifestation of florid psychosis.
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Affiliation(s)
- Daiki Sasabayashi
- Department of Neuropsychiatry, University of Toyama Graduate School of Medicine and Pharmaceutical Sciences, Toyama, Japan
| | - Yoichiro Takayanagi
- Department of Neuropsychiatry, University of Toyama Graduate School of Medicine and Pharmaceutical Sciences, Toyama, Japan
| | - Tsutomu Takahashi
- Department of Neuropsychiatry, University of Toyama Graduate School of Medicine and Pharmaceutical Sciences, Toyama, Japan
| | - Kiyotaka Nemoto
- Department of Psychiatry, Faculty of Medicine, University of Tsukuba, Ibaraki, Japan
| | - Atsushi Furuichi
- Department of Neuropsychiatry, University of Toyama Graduate School of Medicine and Pharmaceutical Sciences, Toyama, Japan
| | - Mikio Kido
- Department of Neuropsychiatry, University of Toyama Graduate School of Medicine and Pharmaceutical Sciences, Toyama, Japan
| | - Yumiko Nishikawa
- Department of Neuropsychiatry, University of Toyama Graduate School of Medicine and Pharmaceutical Sciences, Toyama, Japan
| | - Mihoko Nakamura
- Department of Neuropsychiatry, University of Toyama Graduate School of Medicine and Pharmaceutical Sciences, Toyama, Japan
| | - Kyo Noguchi
- Department of Radiology, University of Toyama Graduate School of Medicine and Pharmaceutical Sciences, Toyama, Japan
| | - Michio Suzuki
- Department of Neuropsychiatry, University of Toyama Graduate School of Medicine and Pharmaceutical Sciences, Toyama, Japan
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40
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Ivleva EI, Turkozer HB, Sweeney JA. Imaging-Based Subtyping for Psychiatric Syndromes. Neuroimaging Clin N Am 2019; 30:35-44. [PMID: 31759570 DOI: 10.1016/j.nic.2019.09.005] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/08/2023]
Abstract
Despite considerable research evidence demonstrating significant neurobiological alterations in psychiatric disorders, incorporating neuroimaging approaches into clinical practice remains challenging. There is an urgent need for biologically validated psychiatric disease constructs that can inform diagnostic algorithms and targeted treatment development. In this article, we present a conceptual review of the most robust and impactful findings from studies that use neuroimaging methods in efforts to define distinct disease subtypes, while emphasizing cross-diagnostic and dimensional approaches. In addition, we discuss current challenges in psychoradiology and outline potential future strategies for clinically applicable translation.
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Affiliation(s)
- Elena I Ivleva
- Department of Psychiatry, UT Southwestern Medical Center, 5323 Harry Hines Boulevard, NC5, Dallas, TX 75390, USA.
| | - Halide B Turkozer
- Department of Psychiatry, UT Southwestern Medical Center, 5323 Harry Hines Boulevard, NC5, Dallas, TX 75390, USA
| | - John A Sweeney
- Department of Psychiatry, University of Cincinnati, 2600 Clifton Avenue, Cincinnati, OH 45221, USA
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41
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Worthington MA, Cao H, Cannon TD. Discovery and Validation of Prediction Algorithms for Psychosis in Youths at Clinical High Risk. BIOLOGICAL PSYCHIATRY: COGNITIVE NEUROSCIENCE AND NEUROIMAGING 2019; 5:738-747. [PMID: 31902580 DOI: 10.1016/j.bpsc.2019.10.006] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/14/2019] [Revised: 10/07/2019] [Accepted: 10/26/2019] [Indexed: 12/19/2022]
Abstract
In the past 2 to 3 decades, clinicians have used the clinical high risk for psychosis (CHR-P) paradigm to better understand factors that contribute to the onset of psychotic disorders. While this paradigm is useful to identify individuals at risk, the CHR-P criteria are not sufficient to predict outcomes from the CHR-P population. Because approximately 25% of the CHR-P population will ultimately convert to psychosis, more precise methods of prediction are needed to account for heterogeneity in both risk factors and outcomes in the CHR-P population. To this end, several groups in recent years have used data-driven approaches to refine predictive algorithms to predict both conversion to psychosis and functional outcomes. These models have generally used either clinical and behavioral data, including demographics and measures of symptom severity, neurocognitive functioning, and social functioning, or neuroimaging data, including structural and functional measures, to predict conversion to psychosis in CHR-P samples. This review focuses on the empirical models that have been derived within each of these lines of research and evaluates the performance and methodology of these models. This review also serves to inform best practices for data-driven approaches and directions moving forward to improve our prediction of psychotic disorders and associated outcomes. Because sample size is still the most critical consideration in the current models, we urge that algorithms to predict conversion be conducted using multisite data in order to obtain the power necessary to conclusively determine predictive accuracy without overfitting.
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Affiliation(s)
| | - Hengyi Cao
- Department of Psychology, Yale University, New Haven, Connecticut
| | - Tyrone D Cannon
- Department of Psychology, Yale University, New Haven, Connecticut.
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42
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Huang X, Gong Q, Sweeney JA, Biswal BB. Progress in psychoradiology, the clinical application of psychiatric neuroimaging. Br J Radiol 2019; 92:20181000. [PMID: 31170803 PMCID: PMC6732936 DOI: 10.1259/bjr.20181000] [Citation(s) in RCA: 42] [Impact Index Per Article: 8.4] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/26/2018] [Revised: 05/09/2019] [Accepted: 05/21/2019] [Indexed: 02/05/2023] Open
Abstract
Psychoradiology is an emerging field that applies radiological imaging technologies to psychiatric conditions. In the past three decades, brain imaging techniques have rapidly advanced understanding of illness and treatment effects in psychiatry. Based on these advances, radiologists have become increasingly interested in applying these advances for differential diagnosis and individualized patient care selection for common psychiatric illnesses. This shift from research to clinical practice represents the beginning evolution of psychoradiology. In this review, we provide a summary of recent progress relevant to this field based on their clinical functions, namely the (1) classification and subtyping; (2) prediction and monitoring of treatment outcomes; and (3) treatment selection. In addition, we provide guidelines for the practice of psychoradiology in clinical settings and suggestions for future research to validate broader clinical applications. Given the high prevalence of psychiatric disorders and the importance of increased participation of radiologists in this field, a guide regarding advances in this field and a description of relevant clinical work flow patterns help radiologists contribute to this fast-evolving field.
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Affiliation(s)
| | | | - John A. Sweeney
- Department of Psychiatry and Behavioral Neuroscience, University of Cincinnati, Cincinnati, USA
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Esworthy TJ, Miao S, Lee SJ, Zhou X, Cui H, Zuo YY, Zhang LG. Advanced 4D Bioprinting Technologies for Brain Tissue Modeling and Study. INTERNATIONAL JOURNAL OF SMART AND NANO MATERIALS 2019; 10:177-204. [PMID: 32864037 PMCID: PMC7451241 DOI: 10.1080/19475411.2019.1631899] [Citation(s) in RCA: 18] [Impact Index Per Article: 3.6] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/22/2018] [Accepted: 06/10/2019] [Indexed: 05/27/2023]
Abstract
Although the process by which the cortical tissues of the brain fold has been the subject of considerable study and debate over the past few decades, a single mechanistic description of the phenomenon has yet to be fully accepted. Rather, two competing explanations of cortical folding have arisen in recent years; known as the axonal tension and the differential tangential expansion models. In the present review, these two models are introduced by analyzing the computational, theoretical, materials-based, and cell studies which have yielded them. Then Four-dimensional bioprinting is presented as a powerful technology which can not only be used to test both models of cortical folding de novo, but can also be used to explore the reciprocal effects that folding associated mechanical stresses may have on neural development. Therein, the fabrication of "smart" tissue models which can accurately simulate the in vivo folding process and recapitulate physiologically relevant stresses are introduced. We also provide a general description of both cortical neurobiology as well as the cellular basis of cortical folding. Our discussion also entails an overview of both 3D and 4D bioprinting technologies, as well as a brief commentary on recent advancements in printed central nervous system tissue engineering.
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Affiliation(s)
- Timothy J Esworthy
- Department of Mechanical and Aerospace Engineering, The George Washington University, Washington DC 20052, USA
| | - Shida Miao
- Department of Mechanical and Aerospace Engineering, The George Washington University, Washington DC 20052, USA
| | - Se-Jun Lee
- Department of Mechanical and Aerospace Engineering, The George Washington University, Washington DC 20052, USA
| | - Xuan Zhou
- Department of Mechanical and Aerospace Engineering, The George Washington University, Washington DC 20052, USA
| | - Haitao Cui
- Department of Mechanical and Aerospace Engineering, The George Washington University, Washington DC 20052, USA
| | - Yi Y Zuo
- Department of Mechanical Engineering, University of Hawaii at Manoa, Honolulu, HI 96822, USA
| | - Lijie Grace Zhang
- Department of Mechanical and Aerospace Engineering, The George Washington University, Washington DC 20052, USA
- Department of Medicine, The George Washington University, Washington DC 20052, USA
- Department of Biomedical Engineering, The George Washington University, Washington DC 20052, USA
- Department of Electrical and Computer Engineering, The George Washington University, Washington DC 20052, USA
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Addington J, Farris M, Stowkowy J, Santesteban-Echarri O, Metzak P, Kalathil MS. Predictors of Transition to Psychosis in Individuals at Clinical High Risk. Curr Psychiatry Rep 2019; 21:39. [PMID: 31037392 DOI: 10.1007/s11920-019-1027-y] [Citation(s) in RCA: 41] [Impact Index Per Article: 8.2] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 01/06/2023]
Abstract
PURPOSE OF REVIEW Current research is examining predictors of the transition to psychosis in youth who are at clinical high risk based on attenuated psychotic symptoms (APS). Determining predictors of the development of psychosis is important for an improved understanding of mechanisms as well as the development of preventative strategies. The purpose is to review the most recent literature identifying predictors of the transition to psychosis in those who are already assessed as being at risk. RECENT FINDINGS Multidomain models, in particular, integrated models of symptoms, social functioning, and cognition variables, achieve better predictive performance than individual factors. There are many methodological issues; however, several solutions have now been described in the literature. For youth who already have APS, predicting who may go on to later develop psychosis is possible. Several studies are underway in large consortiums that may overcome some of the methodological concerns and develop improved means of prediction.
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Affiliation(s)
- Jean Addington
- Hotchkiss Brain Institute, Department of Psychiatry, University of Calgary, Calgary, Alberta, Canada.
| | - Megan Farris
- Hotchkiss Brain Institute, Department of Psychiatry, University of Calgary, Calgary, Alberta, Canada
| | - Jacqueline Stowkowy
- Hotchkiss Brain Institute, Department of Psychiatry, University of Calgary, Calgary, Alberta, Canada
| | - Olga Santesteban-Echarri
- Hotchkiss Brain Institute, Department of Psychiatry, University of Calgary, Calgary, Alberta, Canada
| | - Paul Metzak
- Hotchkiss Brain Institute, Department of Psychiatry, University of Calgary, Calgary, Alberta, Canada
| | - Mohammed Shakeel Kalathil
- Hotchkiss Brain Institute, Department of Psychiatry, University of Calgary, Calgary, Alberta, Canada
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45
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Diaconescu AO, Hauke DJ, Borgwardt S. Models of persecutory delusions: a mechanistic insight into the early stages of psychosis. Mol Psychiatry 2019; 24:1258-1267. [PMID: 31076646 PMCID: PMC6756090 DOI: 10.1038/s41380-019-0427-z] [Citation(s) in RCA: 18] [Impact Index Per Article: 3.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/10/2018] [Revised: 02/27/2019] [Accepted: 04/11/2019] [Indexed: 12/16/2022]
Abstract
Identifying robust markers for predicting the onset of psychosis has been a key challenge for early detection research. Persecutory delusions are core symptoms of psychosis, and social cognition is particularly impaired in first-episode psychosis patients and individuals at risk for developing psychosis. Here, we propose new avenues for translation provided by hierarchical Bayesian models of behaviour and neuroimaging data applied in the context of social learning to target persecutory delusions. As it comprises a mechanistic model embedded in neurophysiology, the findings of this approach may shed light onto inference and neurobiological causes of transition to psychosis.
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Affiliation(s)
- Andreea Oliviana Diaconescu
- Department of Psychiatry (UPK), University of Basel, Basel, Switzerland. .,Translational Neuromodeling Unit (TNU), Institute for Biomedical Engineering, University of Zurich & Swiss Federal Institute of Technology (ETH Zurich), Zurich, Switzerland.
| | - Daniel Jonas Hauke
- 0000 0004 1937 0642grid.6612.3Department of Psychiatry (UPK), University of Basel, Basel, Switzerland ,0000 0004 1937 0642grid.6612.3Department of Mathematics and Computer Science, University of Basel, Basel, Switzerland
| | - Stefan Borgwardt
- 0000 0004 1937 0642grid.6612.3Department of Psychiatry (UPK), University of Basel, Basel, Switzerland ,0000 0001 2322 6764grid.13097.3cDepartment of Psychosis Studies PO63, Institute of Psychiatry, Psychology and Neuroscience, King’s College London, London, UK
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Guloksuz S, van Os J. Need for evidence-based early intervention programmes: a public health perspective. EVIDENCE-BASED MENTAL HEALTH 2018; 21:128-130. [PMID: 30282627 PMCID: PMC10270415 DOI: 10.1136/ebmental-2018-300030] [Citation(s) in RCA: 16] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/18/2018] [Accepted: 08/31/2018] [Indexed: 12/15/2022]
Abstract
This paper attempts to discuss why the early intervention agenda based on the current convention of 'ultra-high risk' (UHR) or 'clinical high risk' (CHR) for 'transition' to psychosis framework has been destined to fall short of generating a measurable and economically feasible public health impact. To summarise: (1) the primary determinant of the 'transition' rate is not the predictive value of the UHR/CHR but the degree of the risk-enrichment; (2) even with a significant pre-test risk enrichment, the prognostic accuracy of the assessment tools in help-seeking population is mediocre, failing to meet the bare minimum thresholds; (3) therapeutic interventions arguably prolong the time-to-onset of psychotic symptoms instead of preventing 'transition', given that the UHR/CHR and 'transition' lie on the same unidimensional scale of positive psychotic symptoms; (4) meta-analytical evidence confirms that specific effective treatment for preventing 'transition' (the goal-primary outcome-of the UHR/CHR framework) is not available; (5) the UHR/CHR-'transition' is a precarious target for research given the unpredictability driven by the sampling strategies and the natural ebb and flow of psychotic symptoms within and between individuals, leading to false positives; (6) only a negligible portion of those who develop psychosis benefits from UHR/CHR services (see prevention paradox); (7) limited data on the cost-effectiveness of these services exist. Given the pitfalls of the narrow focus of the UHR/CHR framework, a broader prevention strategy embracing pluripotency of early psychopathology seems to serve as a better alternative. Nevertheless, there is a need for economic evaluation of these extended transdiagnostic early intervention programmes.
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Affiliation(s)
- Sinan Guloksuz
- Department of Psychiatry and Neuropsychology, School for Mental Health and Neuroscience, Maastricht University Medical Centre, Maastricht, The Netherlands
- Department of Psychiatry, Yale University School of Medicine, New Haven, Connecticut, USA
| | - Jim van Os
- Department of Psychiatry and Neuropsychology, School for Mental Health and Neuroscience, Maastricht University Medical Centre, Maastricht, The Netherlands
- Department of Psychiatry, Brain Centre Rudolf Magnus, University Medical Centre Utrecht, Utrecht, The Netherlands
- Department of Psychosis Studies, King’s College London, King’s Health Partners, Institute of Psychiatry, London, UK
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