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Yu JC, Hawco C, Bassman L, Oliver LD, Argyelan M, Gold JM, Tang SX, Foussias G, Buchanan RW, Malhotra AK, Ameis SH, Voineskos AN, Dickie EW. Multivariate Association between Functional Connectivity Gradients and Cognition in Schizophrenia Spectrum Disorders. BIOLOGICAL PSYCHIATRY. COGNITIVE NEUROSCIENCE AND NEUROIMAGING 2024:S2451-9022(24)00268-4. [PMID: 39260567 DOI: 10.1016/j.bpsc.2024.09.001] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/17/2024] [Revised: 08/21/2024] [Accepted: 09/02/2024] [Indexed: 09/13/2024]
Abstract
BACKGROUND Schizophrenia Spectrum Disorders (SSDs), which are characterized by social cognitive deficits, have been associated with dysconnectivity in "unimodal" (e.g., visual, auditory) and "multimodal" (e.g., default-mode and frontoparietal) cortical networks. However, little is known regarding how such dysconnectivity relates to social and non-social cognition, and how such brain-behavioral relationships associate with clinical outcomes of SSDs. METHODS We analyzed cognitive (non-social and social) measures and resting-state functional magnetic resonance imaging data from the 'Social Processes Initiative in Neurobiology of the Schizophrenia(s) (SPINS)' study (247 stable participants with SSDs and 172 healthy controls, ages 18-55). We extracted gradients from parcellated connectomes and examined the association between the first 3 gradients and the cognitive measures using partial least squares correlation (PLSC). We then correlated the PLSC dimensions with functioning and symptoms in the SSDs group. RESULTS The SSDs group showed significantly lower differentiation on all three gradients. The first PLSC dimension explained 68.53% (p<.001) of the covariance and showed a significant difference between SSDs and Controls (bootstrap p<.05). PLSC showed that all cognitive measures were associated with gradient scores of unimodal and multimodal networks (Gradient 1), auditory, sensorimotor, and visual networks (Gradient 2), and perceptual networks and striatum (Gradient 3), which were less differentiated in SSDs. Furthermore, the first dimension was positively correlated with negative symptoms and functioning in the SSDs group. CONCLUSIONS These results suggest a potential role of lower differentiation of brain networks in cognitive and functional impairments in SSDs.
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Affiliation(s)
- Ju-Chi Yu
- Kimel Family Translational Imaging-Genetics Research Lab, Campbell Family Mental Health Research Institute, Centre for Addiction and Mental Health, Toronto, Canada.
| | - Colin Hawco
- Kimel Family Translational Imaging-Genetics Research Lab, Campbell Family Mental Health Research Institute, Centre for Addiction and Mental Health, Toronto, Canada; University of Toronto, Temerty Faculty of Medicine, Department of Psychiatry, Toronto, Canada
| | - Lucy Bassman
- Kimel Family Translational Imaging-Genetics Research Lab, Campbell Family Mental Health Research Institute, Centre for Addiction and Mental Health, Toronto, Canada
| | - Lindsay D Oliver
- Kimel Family Translational Imaging-Genetics Research Lab, Campbell Family Mental Health Research Institute, Centre for Addiction and Mental Health, Toronto, Canada; University of Toronto, Temerty Faculty of Medicine, Department of Psychiatry, Toronto, Canada
| | | | - James M Gold
- Maryland Psychiatric Research Center, Department of Psychiatry, University of Maryland School of Medicine, Baltimore, MD, USA
| | | | - George Foussias
- Kimel Family Translational Imaging-Genetics Research Lab, Campbell Family Mental Health Research Institute, Centre for Addiction and Mental Health, Toronto, Canada; University of Toronto, Temerty Faculty of Medicine, Department of Psychiatry, Toronto, Canada
| | - Robert W Buchanan
- Maryland Psychiatric Research Center, Department of Psychiatry, University of Maryland School of Medicine, Baltimore, MD, USA
| | | | - Stephanie H Ameis
- Kimel Family Translational Imaging-Genetics Research Lab, Campbell Family Mental Health Research Institute, Centre for Addiction and Mental Health, Toronto, Canada; University of Toronto, Temerty Faculty of Medicine, Department of Psychiatry, Toronto, Canada
| | - Aristotle N Voineskos
- Kimel Family Translational Imaging-Genetics Research Lab, Campbell Family Mental Health Research Institute, Centre for Addiction and Mental Health, Toronto, Canada; University of Toronto, Temerty Faculty of Medicine, Department of Psychiatry, Toronto, Canada
| | - Erin W Dickie
- Kimel Family Translational Imaging-Genetics Research Lab, Campbell Family Mental Health Research Institute, Centre for Addiction and Mental Health, Toronto, Canada; University of Toronto, Temerty Faculty of Medicine, Department of Psychiatry, Toronto, Canada.
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Guo J, He C, Song H, Gao H, Yao S, Dong SS, Yang TL. Unveiling Promising Neuroimaging Biomarkers for Schizophrenia Through Clinical and Genetic Perspectives. Neurosci Bull 2024; 40:1333-1352. [PMID: 38703276 PMCID: PMC11365900 DOI: 10.1007/s12264-024-01214-1] [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: 07/14/2023] [Accepted: 01/08/2024] [Indexed: 05/06/2024] Open
Abstract
Schizophrenia is a complex and serious brain disorder. Neuroscientists have become increasingly interested in using magnetic resonance-based brain imaging-derived phenotypes (IDPs) to investigate the etiology of psychiatric disorders. IDPs capture valuable clinical advantages and hold biological significance in identifying brain abnormalities. In this review, we aim to discuss current and prospective approaches to identify potential biomarkers for schizophrenia using clinical multimodal neuroimaging and imaging genetics. We first described IDPs through their phenotypic classification and neuroimaging genomics. Secondly, we discussed the applications of multimodal neuroimaging by clinical evidence in observational studies and randomized controlled trials. Thirdly, considering the genetic evidence of IDPs, we discussed how can utilize neuroimaging data as an intermediate phenotype to make association inferences by polygenic risk scores and Mendelian randomization. Finally, we discussed machine learning as an optimum approach for validating biomarkers. Together, future research efforts focused on neuroimaging biomarkers aim to enhance our understanding of schizophrenia.
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Affiliation(s)
- Jing Guo
- Key Laboratory of Biomedical Information Engineering of Ministry of Education, Biomedical Informatics and Genomics Center, School of Life Science and Technology, Xi'an Jiaotong University, Xi'an, 710049, China
| | - Changyi He
- Key Laboratory of Biomedical Information Engineering of Ministry of Education, Biomedical Informatics and Genomics Center, School of Life Science and Technology, Xi'an Jiaotong University, Xi'an, 710049, China
| | - Huimiao Song
- Key Laboratory of Biomedical Information Engineering of Ministry of Education, Biomedical Informatics and Genomics Center, School of Life Science and Technology, Xi'an Jiaotong University, Xi'an, 710049, China
| | - Huiwu Gao
- Key Laboratory of Biomedical Information Engineering of Ministry of Education, Biomedical Informatics and Genomics Center, School of Life Science and Technology, Xi'an Jiaotong University, Xi'an, 710049, China
| | - Shi Yao
- Guangdong Key Laboratory of Age-Related Cardiac and Cerebral Diseases, Affiliated Hospital of Guangdong Medical University, Zhanjiang, 524000, China
| | - Shan-Shan Dong
- Key Laboratory of Biomedical Information Engineering of Ministry of Education, Biomedical Informatics and Genomics Center, School of Life Science and Technology, Xi'an Jiaotong University, Xi'an, 710049, China.
| | - Tie-Lin Yang
- Key Laboratory of Biomedical Information Engineering of Ministry of Education, Biomedical Informatics and Genomics Center, School of Life Science and Technology, Xi'an Jiaotong University, Xi'an, 710049, China.
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Taira M, Millard SJ, Verghese A, DiFazio LE, Hoang IB, Jia R, Sias A, Wikenheiser A, Sharpe MJ. Dopamine Release in the Nucleus Accumbens Core Encodes the General Excitatory Components of Learning. J Neurosci 2024; 44:e0120242024. [PMID: 38969504 PMCID: PMC11358529 DOI: 10.1523/jneurosci.0120-24.2024] [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: 01/17/2024] [Revised: 06/18/2024] [Accepted: 06/20/2024] [Indexed: 07/07/2024] Open
Abstract
Dopamine release in the nucleus accumbens core (NAcC) is generally considered to be a proxy for phasic firing of the ventral tegmental area dopamine (VTADA) neurons. Thus, dopamine release in NAcC is hypothesized to reflect a unitary role in reward prediction error signaling. However, recent studies reveal more diverse roles of dopamine neurons, which support an emerging idea that dopamine regulates learning differently in distinct circuits. To understand whether the NAcC might regulate a unique component of learning, we recorded dopamine release in NAcC while male rats performed a backward conditioning task where a reward is followed by a neutral cue. We used this task because we can delineate different components of learning, which include sensory-specific inhibitory and general excitatory components. Furthermore, we have shown that VTADA neurons are necessary for both the specific and general components of backward associations. Here, we found that dopamine release in NAcC increased to the reward across learning while reducing to the cue that followed as it became more expected. This mirrors the dopamine prediction error signal seen during forward conditioning and cannot be accounted for temporal-difference reinforcement learning. Subsequent tests allowed us to dissociate these learning components and revealed that dopamine release in NAcC reflects the general excitatory component of backward associations, but not their sensory-specific component. These results emphasize the importance of examining distinct functions of different dopamine projections in reinforcement learning.
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Affiliation(s)
- Masakazu Taira
- Department of Psychology, University of Sydney, Camperdown, New South Wales 2006, Australia
- Department of Psychology, University of California, Los Angeles 90095, California
| | - Samuel J Millard
- Department of Psychology, University of California, Los Angeles 90095, California
| | - Anna Verghese
- Department of Psychology, University of California, Los Angeles 90095, California
| | - Lauren E DiFazio
- Department of Psychology, University of California, Los Angeles 90095, California
| | - Ivy B Hoang
- Department of Psychology, University of California, Los Angeles 90095, California
| | - Ruiting Jia
- Department of Psychology, University of California, Los Angeles 90095, California
| | - Ana Sias
- Department of Psychology, University of California, Los Angeles 90095, California
| | - Andrew Wikenheiser
- Department of Psychology, University of California, Los Angeles 90095, California
| | - Melissa J Sharpe
- Department of Psychology, University of Sydney, Camperdown, New South Wales 2006, Australia
- Department of Psychology, University of California, Los Angeles 90095, California
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Li X, Zeng J, Liu N, Yang C, Tao B, Sun H, Gong Q, Zhang W, Li CSR, Lui S. Progressive alterations of resting-state hypothalamic dysconnectivity in schizophrenia. Prog Neuropsychopharmacol Biol Psychiatry 2024; 135:111127. [PMID: 39181307 DOI: 10.1016/j.pnpbp.2024.111127] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/15/2024] [Revised: 08/07/2024] [Accepted: 08/20/2024] [Indexed: 08/27/2024]
Abstract
BACKGROUND The hypothalamus may be involved in the pathogenesis of schizophrenia. Investigating hypothalamus dysfunction in schizophrenia and probing how it is related to symptoms and responds to antipsychotic medication is crucial for understanding the potential mechanism of hypothalamus dysfunction under the long-term illness. METHODS We recruited 216 patients with schizophrenia, including 140 antipsychotic-naïve first-episode patients (FES, including 44 patients with 1-year follow-up data), 76 chronically treated schizophrenia (CTS), and 210 healthy controls (HC). Hypothalamic seed-based functional connectivity (FC) was calculated and compared among the FES, CTS, and HC groups using analysis of covariance. Exploratory analysis was conducted between the FES patients at baseline and after 1-year follow-up. Significantly altered hypothalamic FCs were then related to clinical symptomology, while age- and illness-related regression analyses were also conducted and compared between diagnostic groups. RESULTS The FES patients showed decreased hypothalamic FCs with the midbrain and right thalamus, whereas the CTS patients showed more severe decreased hypothalamic FCs with the midbrain, right thalamus, left putamen, right caudate, and bilateral anterior cingulate cortex compared to HCs. These abnormalities were not correlated to the symptomology or illness duration, or not reversed by the antipsychotic treatment. Age-related hypothalamic FC decrease was also identified in the abovementioned regions, and a faster age-related decline of the hypothalamic FC was observed with the left putamen and bilateral anterior cingulate cortex. CONCLUSION Age-related hypothalamic FC decrease extends the functional alterations that characterize the neurodegenerative nature of schizophrenia. Future studies are required to further probe the hormonal or endocrinal underpinnings of such alterations and trace the precise progressive trajectories.
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Affiliation(s)
- Xing Li
- Department of Radiology, and Functional and Molecular Imaging Key Laboratory of Sichuan Province, West China Hospital of Sichuan University, Chengdu, China; Huaxi MR Research Center (HMRRC), West China Hospital of Sichuan University, Chengdu, China; Research Unit of Psychoradiology, Chinese Academy of Medical Sciences, Chengdu, China
| | - Jiaxin Zeng
- Department of Radiology, and Functional and Molecular Imaging Key Laboratory of Sichuan Province, West China Hospital of Sichuan University, Chengdu, China; Huaxi MR Research Center (HMRRC), West China Hospital of Sichuan University, Chengdu, China; Research Unit of Psychoradiology, Chinese Academy of Medical Sciences, Chengdu, China
| | - Naici Liu
- Department of Radiology, and Functional and Molecular Imaging Key Laboratory of Sichuan Province, West China Hospital of Sichuan University, Chengdu, China; Huaxi MR Research Center (HMRRC), West China Hospital of Sichuan University, Chengdu, China; Research Unit of Psychoradiology, Chinese Academy of Medical Sciences, Chengdu, China
| | - Chengmin Yang
- Department of Radiology, and Functional and Molecular Imaging Key Laboratory of Sichuan Province, West China Hospital of Sichuan University, Chengdu, China; Huaxi MR Research Center (HMRRC), West China Hospital of Sichuan University, Chengdu, China; Research Unit of Psychoradiology, Chinese Academy of Medical Sciences, Chengdu, China
| | - Bo Tao
- Department of Radiology, and Functional and Molecular Imaging Key Laboratory of Sichuan Province, West China Hospital of Sichuan University, Chengdu, China; Huaxi MR Research Center (HMRRC), West China Hospital of Sichuan University, Chengdu, China; Research Unit of Psychoradiology, Chinese Academy of Medical Sciences, Chengdu, China
| | - Hui Sun
- Department of Radiology, and Functional and Molecular Imaging Key Laboratory of Sichuan Province, West China Hospital of Sichuan University, Chengdu, China; Huaxi MR Research Center (HMRRC), West China Hospital of Sichuan University, Chengdu, China; Research Unit of Psychoradiology, Chinese Academy of Medical Sciences, Chengdu, China
| | - Qiyong Gong
- Department of Radiology, and Functional and Molecular Imaging Key Laboratory of Sichuan Province, West China Hospital of Sichuan University, Chengdu, China; Huaxi MR Research Center (HMRRC), West China Hospital of Sichuan University, Chengdu, China; Research Unit of Psychoradiology, Chinese Academy of Medical Sciences, Chengdu, China
| | - Wenjing Zhang
- Department of Radiology, and Functional and Molecular Imaging Key Laboratory of Sichuan Province, West China Hospital of Sichuan University, Chengdu, China; Huaxi MR Research Center (HMRRC), West China Hospital of Sichuan University, Chengdu, China; Research Unit of Psychoradiology, Chinese Academy of Medical Sciences, Chengdu, China.
| | - Chiang-Shan R Li
- Departments of Psychiatry and of Neuroscience, Yale University School of Medicine, New Haven, CT, USA.
| | - Su Lui
- Department of Radiology, and Functional and Molecular Imaging Key Laboratory of Sichuan Province, West China Hospital of Sichuan University, Chengdu, China; Huaxi MR Research Center (HMRRC), West China Hospital of Sichuan University, Chengdu, China; Research Unit of Psychoradiology, Chinese Academy of Medical Sciences, Chengdu, China.
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Hu W, Ran X, Wu Z, Zhu H, Kou Y, Zhang S, Yang G, Li W, Yang Y, Lv L, Zhang Y. Short-term antipsychotic treatment reduces functional connectivity of the striatum in first-episode drug-naïve early-onset schizophrenia. Schizophr Res 2024; 270:281-288. [PMID: 38944974 DOI: 10.1016/j.schres.2024.06.016] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/30/2023] [Revised: 04/24/2024] [Accepted: 06/15/2024] [Indexed: 07/02/2024]
Abstract
BACKGROUND The striatum is thought to play a critical role in the pathophysiology and antipsychotic treatment of schizophrenia. Previous studies have revealed abnormal functional connectivity (FC) of the striatum in early-onset schizophrenia (EOS) patients. However, no prior studies have examined post-treatment changes of striatal FC in EOS patients. METHODS We recruited 49 first-episode drug-naïve EOS patients to have resting-state functional magnetic resonance imaging scans at baseline and after 8 weeks of treatment with antipsychotics, along with baseline scanning of 34 healthy controls (HCs) for comparison purposes. We examined the FC values between each seed in striatal subregion and the rest of the brain. The Positive and Negative Syndrome Scale (PANSS) was applied to measure psychiatric symptoms in patients. RESULTS Compared with HCs at baseline, EOS patients exhibited weaker FC of striatal subregions with several brain regions of the salience network and default mode network. Meanwhile, FC between the dorsal caudal putamen (DCP) and left supplementary motor area, as well as between the DCP and right postcentral gyrus, was negatively correlated with PANSS negative scores. Furthermore, after 8 weeks of treatment, EOS patients showed decreased FC between subregions of the putamen and the triangular part of inferior frontal gyrus, middle frontal gyrus, supramarginal gyrus and inferior parietal lobule. CONCLUSIONS Decreased striatal FC is evident, even in the early stages of schizophrenia, and enhance our understanding of the neurodevelopmental abnormalities in schizophrenia. The findings also demonstrate that reduced striatal FC occurs after antipsychotic therapy, indicating that antipsychotic effects need to be accounted for when considering striatal FC abnormalities in schizophrenia.
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Affiliation(s)
- Wenyan Hu
- Department of Psychiatry, Henan Mental Hospital, The Second Affiliated Hospital of Xinxiang Medical University, Xinxiang 453002, China; Henan Key Lab of Biological Psychiatry, Xinxiang Medical University, Xinxiang 453002, China; International Joint Research Laboratory for Psychiatry and Neuroscience of Henan, Xinxiang 453002, China; Henan Collaborative Innovation Center of Prevention and Treatment of Mental Disorder, Xinxiang 453002, China
| | - Xiangying Ran
- School of Medical Engineering, Xinxiang Medical University, Xinxiang 453003, China
| | - Zhaoyang Wu
- Department of Psychiatry, Henan Mental Hospital, The Second Affiliated Hospital of Xinxiang Medical University, Xinxiang 453002, China; Henan Key Lab of Biological Psychiatry, Xinxiang Medical University, Xinxiang 453002, China; International Joint Research Laboratory for Psychiatry and Neuroscience of Henan, Xinxiang 453002, China; Henan Collaborative Innovation Center of Prevention and Treatment of Mental Disorder, Xinxiang 453002, China
| | - Hanyu Zhu
- Department of Psychiatry, Henan Mental Hospital, The Second Affiliated Hospital of Xinxiang Medical University, Xinxiang 453002, China; Henan Key Lab of Biological Psychiatry, Xinxiang Medical University, Xinxiang 453002, China; International Joint Research Laboratory for Psychiatry and Neuroscience of Henan, Xinxiang 453002, China; Henan Collaborative Innovation Center of Prevention and Treatment of Mental Disorder, Xinxiang 453002, China
| | - Yanna Kou
- Department of Psychiatry, Henan Mental Hospital, The Second Affiliated Hospital of Xinxiang Medical University, Xinxiang 453002, China; Henan Key Lab of Biological Psychiatry, Xinxiang Medical University, Xinxiang 453002, China; International Joint Research Laboratory for Psychiatry and Neuroscience of Henan, Xinxiang 453002, China; Henan Collaborative Innovation Center of Prevention and Treatment of Mental Disorder, Xinxiang 453002, China
| | - Sen Zhang
- Department of Psychiatry, Henan Mental Hospital, The Second Affiliated Hospital of Xinxiang Medical University, Xinxiang 453002, China; Henan Key Lab of Biological Psychiatry, Xinxiang Medical University, Xinxiang 453002, China; International Joint Research Laboratory for Psychiatry and Neuroscience of Henan, Xinxiang 453002, China; Henan Collaborative Innovation Center of Prevention and Treatment of Mental Disorder, Xinxiang 453002, China
| | - Ge Yang
- Department of Psychiatry, Henan Mental Hospital, The Second Affiliated Hospital of Xinxiang Medical University, Xinxiang 453002, China; Henan Key Lab of Biological Psychiatry, Xinxiang Medical University, Xinxiang 453002, China; International Joint Research Laboratory for Psychiatry and Neuroscience of Henan, Xinxiang 453002, China; Henan Collaborative Innovation Center of Prevention and Treatment of Mental Disorder, Xinxiang 453002, China
| | - Wenqiang Li
- Department of Psychiatry, Henan Mental Hospital, The Second Affiliated Hospital of Xinxiang Medical University, Xinxiang 453002, China; Henan Key Lab of Biological Psychiatry, Xinxiang Medical University, Xinxiang 453002, China; International Joint Research Laboratory for Psychiatry and Neuroscience of Henan, Xinxiang 453002, China; Henan Collaborative Innovation Center of Prevention and Treatment of Mental Disorder, Xinxiang 453002, China
| | - Yongfeng Yang
- Department of Psychiatry, Henan Mental Hospital, The Second Affiliated Hospital of Xinxiang Medical University, Xinxiang 453002, China; Henan Key Lab of Biological Psychiatry, Xinxiang Medical University, Xinxiang 453002, China; International Joint Research Laboratory for Psychiatry and Neuroscience of Henan, Xinxiang 453002, China; Henan Collaborative Innovation Center of Prevention and Treatment of Mental Disorder, Xinxiang 453002, China
| | - Luxian Lv
- Department of Psychiatry, Henan Mental Hospital, The Second Affiliated Hospital of Xinxiang Medical University, Xinxiang 453002, China; Henan Key Lab of Biological Psychiatry, Xinxiang Medical University, Xinxiang 453002, China; International Joint Research Laboratory for Psychiatry and Neuroscience of Henan, Xinxiang 453002, China; Henan Collaborative Innovation Center of Prevention and Treatment of Mental Disorder, Xinxiang 453002, China.
| | - Yan Zhang
- Department of Psychiatry, Henan Mental Hospital, The Second Affiliated Hospital of Xinxiang Medical University, Xinxiang 453002, China; Henan Key Lab of Biological Psychiatry, Xinxiang Medical University, Xinxiang 453002, China; International Joint Research Laboratory for Psychiatry and Neuroscience of Henan, Xinxiang 453002, China; Henan Collaborative Innovation Center of Prevention and Treatment of Mental Disorder, Xinxiang 453002, China.
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Chopra S, Levi PT, Holmes A, Orchard ER, Segal A, Francey SM, O'Donoghue B, Cropley VL, Nelson B, Graham J, Baldwin L, Yuen HP, Allott K, Alvarez-Jimenez M, Harrigan S, Pantelis C, Wood SJ, McGorry P, Fornito A. Brain-wide Anatomical Connectivity and Prediction of Longitudinal Outcomes in Antipsychotic-Naïve First Episode Psychosis. Biol Psychiatry 2024:S0006-3223(24)01483-5. [PMID: 39069164 DOI: 10.1016/j.biopsych.2024.07.016] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/17/2024] [Revised: 06/05/2024] [Accepted: 07/16/2024] [Indexed: 07/30/2024]
Abstract
BACKGROUND Disruptions of axonal connectivity are thought to be a core pathophysiological feature of psychotic illness, but whether they are present early in the illness, prior to antipsychotic exposure, and whether they can predict clinical outcome remains unknown. METHODS We acquired diffusion-weighted MRI to map structural connectivity between each pair of 319 parcellated brain regions in 61 antipsychotic-naive individuals with First Episode Psychosis (FEP; 15-25 years, 46% female) and a demographically matched sample of 27 control participants, along with clinical follow-up data in patients three months and 12 months after the scan. We used connectome-wide analyses to map disruptions of inter-regional pairwise connectivity and connectome-based predictive modelling to predict longitudinal change in symptoms and functioning. RESULTS Individuals with FEP showed disrupted connectivity in a brain-wide network linking all brain regions when compared with controls (pFWE=.03). Baseline structural connectivity significantly predicted change in functioning over 12 months (r=.44;pFWE=.041), such that lower connectivity within fronto-striato-thalamic systems predicted worse functional outcomes. CONCLUSIONS Brain-wide reductions of structural connectivity exist during the early stages of psychotic illness and cannot be attributed to antipsychotic medication. Moreover, baseline measures of structural connectivity can predict change in patient functional outcomes up to one year after engagement with treatment services.
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Affiliation(s)
- Sidhant Chopra
- Turner Institute for Brain and Mental Health, School of Psychological Sciences, Monash University, Clayton, Australia; Monash Biomedical Imaging, Monash University, Clayton, Australia; Department of Psychology, Yale University, New Haven, USA.
| | - Priscila T Levi
- Turner Institute for Brain and Mental Health, School of Psychological Sciences, Monash University, Clayton, Australia; Monash Biomedical Imaging, Monash University, Clayton, Australia
| | - Alexander Holmes
- Turner Institute for Brain and Mental Health, School of Psychological Sciences, Monash University, Clayton, Australia; Monash Biomedical Imaging, Monash University, Clayton, Australia
| | | | - Ashlea Segal
- Turner Institute for Brain and Mental Health, School of Psychological Sciences, Monash University, Clayton, Australia; Monash Biomedical Imaging, Monash University, Clayton, Australia; Wu Tsai Institute, Department of Neuroscience, Yale University, New Haven, USA
| | - Shona M Francey
- Orygen, Parkville, Australia; Centre for Youth Mental Health, The University of Melbourne, Melbourne, Australia
| | - Brian O'Donoghue
- Centre for Youth Mental Health, The University of Melbourne, Melbourne, Australia; St Vincent's University Hospital, Elm Park, Dublin 4, Ireland; Department of Psychiatry, University College, Dublin, Ireland
| | - Vanessa L Cropley
- Melbourne Neuropsychiatry Centre, Department of Psychiatry, University of Melbourne
| | - Barnaby Nelson
- Orygen, Parkville, Australia; Centre for Youth Mental Health, The University of Melbourne, Melbourne, Australia
| | - Jessica Graham
- Orygen, Parkville, Australia; Centre for Youth Mental Health, The University of Melbourne, Melbourne, Australia
| | - Lara Baldwin
- Orygen, Parkville, Australia; Centre for Youth Mental Health, The University of Melbourne, Melbourne, Australia
| | - Hok Pan Yuen
- Orygen, Parkville, Australia; Centre for Youth Mental Health, The University of Melbourne, Melbourne, Australia
| | - Kelly Allott
- Orygen, Parkville, Australia; Centre for Youth Mental Health, The University of Melbourne, Melbourne, Australia
| | - Mario Alvarez-Jimenez
- Orygen, Parkville, Australia; Centre for Youth Mental Health, The University of Melbourne, Melbourne, Australia
| | - Susy Harrigan
- Orygen, Parkville, Australia; Centre for Youth Mental Health, The University of Melbourne, Melbourne, Australia; Centre for Mental Health, Melbourne School of Global and Population Health, The University of Melbourne, Parkville, Australian
| | - Christos Pantelis
- Melbourne Neuropsychiatry Centre, Department of Psychiatry, University of Melbourne; Western Hospital Sunshine, St Albans, Victoria, Australia
| | - Stephen J Wood
- Orygen, Parkville, Australia; Centre for Youth Mental Health, The University of Melbourne, Melbourne, Australia; School of Psychology, University of Birmingham, Edgbaston, UK
| | - Patrick McGorry
- Orygen, Parkville, Australia; Centre for Youth Mental Health, The University of Melbourne, Melbourne, Australia
| | - Alex Fornito
- Turner Institute for Brain and Mental Health, School of Psychological Sciences, Monash University, Clayton, Australia; Monash Biomedical Imaging, Monash University, Clayton, Australia
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7
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Bagheri S, Yu JC, Gallucci J, Tan V, Oliver LD, Dickie EW, Rashidi AG, Foussias G, Lai MC, Buchanan RW, Malhotra AK, Voineskos AN, Ameis SH, Hawco C. Transdiagnostic Neurobiology of Social Cognition and Individual Variability as Measured by Fractional Amplitude of Low-Frequency Fluctuation in Schizophrenia and Autism Spectrum Disorders. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.07.02.601737. [PMID: 39005278 PMCID: PMC11245004 DOI: 10.1101/2024.07.02.601737] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 07/16/2024]
Abstract
Fractional amplitude of low-frequency fluctuation (fALFF) is a validated measure of resting-state spontaneous brain activity. Previous fALFF findings in autism and schizophrenia spectrum disorders (ASDs and SSDs) have been highly heterogeneous. We aimed to use fALFF in a large sample of typically developing control (TDC), ASD and SSD participants to explore group differences and relationships with inter-individual variability of fALFF maps and social cognition. fALFF from 495 participants (185 TDC, 68 ASD, and 242 SSD) was computed using functional magnetic resonance imaging as signal power within two frequency bands (i.e., slow-4 and slow-5), normalized by the power in the remaining frequency spectrum. Permutation analysis of linear models was employed to investigate the relationship of fALFF with diagnostic groups, higher-level social cognition, and lower-level social cognition. Each participant's average distance of fALFF map to all others was defined as a variability score, with higher scores indicating less typical maps. Lower fALFF in the visual and higher fALFF in the frontal regions were found in both SSD and ASD participants compared with TDCs. Limited differences were observed between ASD and SSD participants in the cuneus regions only. Associations between slow-4 fALFF and higher-level social cognitive scores across the whole sample were observed in the lateral occipitotemporal and temporoparietal junction. Individual variability within the ASD and SSD groups was also significantly higher compared with TDC. Similar patterns of fALFF and individual variability in ASD and SSD suggest some common neurobiological deficits across these related heterogeneous conditions.
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Affiliation(s)
- Soroush Bagheri
- Campbell Family Mental Health Research Institute, Centre for Addiction and Mental Health, Toronto, Ontario, Canada
| | - Ju-Chi Yu
- Campbell Family Mental Health Research Institute, Centre for Addiction and Mental Health, Toronto, Ontario, Canada
| | - Julia Gallucci
- Campbell Family Mental Health Research Institute, Centre for Addiction and Mental Health, Toronto, Ontario, Canada
- Institute of Medical Science, University of Toronto, Toronto, ON, Canada
| | - Vinh Tan
- Campbell Family Mental Health Research Institute, Centre for Addiction and Mental Health, Toronto, Ontario, Canada
- Institute of Medical Science, University of Toronto, Toronto, ON, Canada
| | - Lindsay D. Oliver
- Campbell Family Mental Health Research Institute, Centre for Addiction and Mental Health, Toronto, Ontario, Canada
- Department of Psychiatry, Temerty Faculty of Medicine, University of Toronto, Toronto, Ontario, Canada
| | - Erin W. Dickie
- Campbell Family Mental Health Research Institute, Centre for Addiction and Mental Health, Toronto, Ontario, Canada
- Department of Psychiatry, Temerty Faculty of Medicine, University of Toronto, Toronto, Ontario, Canada
| | - Ayesha G. Rashidi
- Campbell Family Mental Health Research Institute, Centre for Addiction and Mental Health, Toronto, Ontario, Canada
- Institute of Medical Science, University of Toronto, Toronto, ON, Canada
| | - George Foussias
- Campbell Family Mental Health Research Institute, Centre for Addiction and Mental Health, Toronto, Ontario, Canada
- Department of Psychiatry, Temerty Faculty of Medicine, University of Toronto, Toronto, Ontario, Canada
- Institute of Medical Science, University of Toronto, Toronto, ON, Canada
| | - Meng-Chuan Lai
- Campbell Family Mental Health Research Institute, Centre for Addiction and Mental Health, Toronto, Ontario, Canada
- Department of Psychiatry, Temerty Faculty of Medicine, University of Toronto, Toronto, Ontario, Canada
- Institute of Medical Science, University of Toronto, Toronto, ON, Canada
- Research Institute, and Department of Psychiatry, The Hospital for Sick Children, Toronto, ON, Canada
- Department of Psychology, University of Toronto, Toronto, Ontario, Canada
- Autism Research Centre, Department of Psychiatry, University of Cambridge, Cambridge, United Kingdom
- Department of Psychiatry, National Taiwan University Hospital and College of Medicine, Taipei, Taiwan
| | - Robert W. Buchanan
- Maryland Psychiatric Research Centre, Department of Psychiatry, University of Maryland School of Medicine, Baltimore, Maryland, USA
| | - Anil K. Malhotra
- Division of Psychiatry Research, The Zucker Hillside Hospital, Division of Northwell Health, Glen Oaks, NY, USA
- The Donald and Barbara Zucker School of Medicine at Hofstra/Northwell, Department of Psychiatry, Hempstead, NY, USA
- Centre for Psychiatric Neuroscience, The Feinstein Institute for Medical Research, Manhasset, NY, USA
| | - Aristotle N. Voineskos
- Campbell Family Mental Health Research Institute, Centre for Addiction and Mental Health, Toronto, Ontario, Canada
- Department of Psychiatry, Temerty Faculty of Medicine, University of Toronto, Toronto, Ontario, Canada
- Institute of Medical Science, University of Toronto, Toronto, ON, Canada
| | - Stephanie H. Ameis
- Campbell Family Mental Health Research Institute, Centre for Addiction and Mental Health, Toronto, Ontario, Canada
- Department of Psychiatry, Temerty Faculty of Medicine, University of Toronto, Toronto, Ontario, Canada
- Research Institute, and Department of Psychiatry, The Hospital for Sick Children, Toronto, ON, Canada
| | - Colin Hawco
- Campbell Family Mental Health Research Institute, Centre for Addiction and Mental Health, Toronto, Ontario, Canada
- Department of Psychiatry, Temerty Faculty of Medicine, University of Toronto, Toronto, Ontario, Canada
- Institute of Medical Science, University of Toronto, Toronto, ON, Canada
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8
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Agid O, Crespo-Facorro B, de Bartolomeis A, Fagiolini A, Howes OD, Seppälä N, Correll CU. Overcoming the barriers to identifying and managing treatment-resistant schizophrenia and to improving access to clozapine: A narrative review and recommendation for clinical practice. Eur Neuropsychopharmacol 2024; 84:35-47. [PMID: 38657339 DOI: 10.1016/j.euroneuro.2024.04.012] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/05/2024] [Revised: 04/08/2024] [Accepted: 04/10/2024] [Indexed: 04/26/2024]
Abstract
Clozapine is the only approved antipsychotic for treatment-resistant schizophrenia (TRS). Although a large body of evidence supports its efficacy and favorable risk-benefit ratio in individuals who have failed two or more antipsychotics, clozapine remains underused. However, variations in clozapine utilization across geographic and clinical settings suggest that it could be possible to improve its use. In this narrative review and expert opinion, we summarized information available in the literature on the mechanisms of action, effectiveness, and potential adverse events of clozapine. We identified barriers leading to discouragement in clozapine prescription internationally, and we proposed practical solutions to overcome each barrier. One of the main obstacles identified to the use of clozapine is the lack of appropriate training for physicians: we highlighted the need to develop specific professional programs to train clinicians, both practicing and in residency, on the relevance and efficacy of clozapine in TRS treatment, initiation, maintenance, and management of potential adverse events. This approach would facilitate physicians to identify eligible patients and offer clozapine as a treatment option in the early stage of the disease. We also noted that increasing awareness of the benefits of clozapine among healthcare professionals, people with TRS, and their caregivers can help promote the use of clozapine. Educational material, such as leaflets or videos, could be developed and distributed to achieve this goal. The information provided in this article may be useful to improve disease burden and support healthcare professionals, patients, and caregivers navigating the complex pathways to TRS management.
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Affiliation(s)
- Ofer Agid
- Centre for Addiction and Mental Health, University of Toronto, Canada
| | - Benedicto Crespo-Facorro
- Department of Psychiatry, School of Medicine, University Hospital Virgen del Rocío-IBiS-CSIC, Sevilla, Spain, Centro de Investigación Biomédica en Red en Salud Mental (CIBERSAM), Sevilla, Spain
| | - Andrea de Bartolomeis
- University of Naples Federico II, Department of Neuroscience, Reproductive Science, and Odontostomatology. Laboratory of Molecular and Translational Psychiatry. Unit of Treatment Resistant Psychosis, Naples, Italy; Staff Unesco Chair at University of Naples Federico II, Italy
| | | | - Oliver D Howes
- IoPPN, King's College London, De Crespigny Park, London, United Kingdom; Institute of Clinical Sciences (ICS), Faculty of Medicine, Imperial College London, Du Cane Road, London, United Kingdom
| | - Niko Seppälä
- Wellbeing Services in Satakunta, Department of Psychiatry, Pori, Finland and Medical Consultant, Viatris, Finland
| | - Christoph U Correll
- The Zucker Hillside Hospital, Department of Psychiatry, Northwell Health, Glen Oaks, New York, United States; Donald and Barbara Zucker School of Medicine at Hofstra/Northwell, Department of Psychiatry and Molecular Medicine, Hempstead, New York, United States; Charité - Universitätsmedizin Berlin, Department of Child and Adolescent Psychiatry, Augustenburger Platz 1, Berlin 13353, Germany; German Center for Mental Health (DZPG), Partner Site Berlin, Berlin, Germany.
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9
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Sun H, Liu N, Qiu C, Tao B, Yang C, Tang B, Li H, Zhan K, Cai C, Zhang W, Lui S. Applications of MRI in Schizophrenia: Current Progress in Establishing Clinical Utility. J Magn Reson Imaging 2024. [PMID: 38946400 DOI: 10.1002/jmri.29470] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/17/2023] [Revised: 05/20/2024] [Accepted: 05/20/2024] [Indexed: 07/02/2024] Open
Abstract
Schizophrenia is a severe mental illness that significantly impacts the lives of affected individuals and with increasing mortality rates. Early detection and intervention are crucial for improving outcomes but the lack of validated biomarkers poses great challenges in such efforts. The use of magnetic resonance imaging (MRI) in schizophrenia enables the investigation of the disorder's etiological and neuropathological substrates in vivo. After decades of research, promising findings of MRI have been shown to aid in screening high-risk individuals and predicting illness onset, and predicting symptoms and treatment outcomes of schizophrenia. The integration of machine learning and deep learning techniques makes it possible to develop intelligent diagnostic and prognostic tools with extracted or selected imaging features. In this review, we aimed to provide an overview of current progress and prospects in establishing clinical utility of MRI in schizophrenia. We first provided an overview of MRI findings of brain abnormalities that might underpin the symptoms or treatment response process in schizophrenia patients. Then, we summarized the ongoing efforts in the computer-aided utility of MRI in schizophrenia and discussed the gap between MRI research findings and real-world applications. Finally, promising pathways to promote clinical translation were provided. EVIDENCE LEVEL: 2 TECHNICAL EFFICACY: Stage 3.
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Affiliation(s)
- Hui Sun
- Department of Radiology, and Functional and Molecular Imaging Key Laboratory of Sichuan Province, West China Hospital of Sichuan University, Chengdu, China
- Huaxi MR Research Center (HMRRC), West China Hospital of Sichuan University, Chengdu, China
- Research Unit of Psychoradiology, Chinese Academy of Medical Sciences, Chengdu, China
| | - Naici Liu
- Department of Radiology, and Functional and Molecular Imaging Key Laboratory of Sichuan Province, West China Hospital of Sichuan University, Chengdu, China
- Huaxi MR Research Center (HMRRC), West China Hospital of Sichuan University, Chengdu, China
- Research Unit of Psychoradiology, Chinese Academy of Medical Sciences, Chengdu, China
| | - Changjian Qiu
- Mental Health Center, West China Hospital of Sichuan University, Sichuan Clinical Medical Research Center for Mental Disorders, Chengdu, China
| | - Bo Tao
- Department of Radiology, and Functional and Molecular Imaging Key Laboratory of Sichuan Province, West China Hospital of Sichuan University, Chengdu, China
- Huaxi MR Research Center (HMRRC), West China Hospital of Sichuan University, Chengdu, China
- Research Unit of Psychoradiology, Chinese Academy of Medical Sciences, Chengdu, China
| | - Chengmin Yang
- Department of Radiology, and Functional and Molecular Imaging Key Laboratory of Sichuan Province, West China Hospital of Sichuan University, Chengdu, China
- Huaxi MR Research Center (HMRRC), West China Hospital of Sichuan University, Chengdu, China
- Research Unit of Psychoradiology, Chinese Academy of Medical Sciences, Chengdu, China
| | - Biqiu Tang
- Department of Radiology, and Functional and Molecular Imaging Key Laboratory of Sichuan Province, West China Hospital of Sichuan University, Chengdu, China
- Huaxi MR Research Center (HMRRC), West China Hospital of Sichuan University, Chengdu, China
- Research Unit of Psychoradiology, Chinese Academy of Medical Sciences, Chengdu, China
| | - Hongwei Li
- Department of Radiology, and Functional and Molecular Imaging Key Laboratory of Sichuan Province, West China Hospital of Sichuan University, Chengdu, China
- Huaxi MR Research Center (HMRRC), West China Hospital of Sichuan University, Chengdu, China
- Research Unit of Psychoradiology, Chinese Academy of Medical Sciences, Chengdu, China
- Department of Radiology, The Third Hospital of Mianyang/Sichuan Mental Health Center, Mianyang, China
| | - Kongcai Zhan
- Department of Radiology, Zigong Affiliated Hospital of Southwest Medical University, Zigong Psychiatric Research Center, Zigong, China
| | - Chunxian Cai
- Department of Radiology, the Second People's Hospital of Neijiang, Neijiang, China
| | - Wenjing Zhang
- Department of Radiology, and Functional and Molecular Imaging Key Laboratory of Sichuan Province, West China Hospital of Sichuan University, Chengdu, China
- Huaxi MR Research Center (HMRRC), West China Hospital of Sichuan University, Chengdu, China
- Research Unit of Psychoradiology, Chinese Academy of Medical Sciences, Chengdu, China
| | - Su Lui
- Department of Radiology, and Functional and Molecular Imaging Key Laboratory of Sichuan Province, West China Hospital of Sichuan University, Chengdu, China
- Huaxi MR Research Center (HMRRC), West China Hospital of Sichuan University, Chengdu, China
- Research Unit of Psychoradiology, Chinese Academy of Medical Sciences, Chengdu, China
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10
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Haddon JE, Titherage D, Heckenast JR, Carter J, Owen MJ, Hall J, Wilkinson LS, Jones MW. Linking haploinsufficiency of the autism- and schizophrenia-associated gene Cyfip1 with striatal-limbic-cortical network dysfunction and cognitive inflexibility. Transl Psychiatry 2024; 14:256. [PMID: 38876996 PMCID: PMC11178837 DOI: 10.1038/s41398-024-02969-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/27/2023] [Revised: 05/01/2024] [Accepted: 05/29/2024] [Indexed: 06/16/2024] Open
Abstract
Impaired behavioural flexibility is a core feature of neuropsychiatric disorders and is associated with underlying dysfunction of fronto-striatal circuitry. Reduced dosage of Cyfip1 is a risk factor for neuropsychiatric disorder, as evidenced by its involvement in the 15q11.2 (BP1-BP2) copy number variant: deletion carriers are haploinsufficient for CYFIP1 and exhibit a two- to four-fold increased risk of schizophrenia, autism and/or intellectual disability. Here, we model the contributions of Cyfip1 to behavioural flexibility and related fronto-striatal neural network function using a recently developed haploinsufficient, heterozygous knockout rat line. Using multi-site local field potential (LFP) recordings during resting state, we show that Cyfip1 heterozygous rats (Cyfip1+/-) harbor disrupted network activity spanning medial prefrontal cortex, hippocampal CA1 and ventral striatum. In particular, Cyfip1+/- rats showed reduced influence of nucleus accumbens and increased dominance of prefrontal and hippocampal inputs, compared to wildtype controls. Adult Cyfip1+/- rats were able to learn a single cue-response association, yet unable to learn a conditional discrimination task that engages fronto-striatal interactions during flexible pairing of different levers and cue combinations. Together, these results implicate Cyfip1 in development or maintenance of cortico-limbic-striatal network integrity, further supporting the hypothesis that alterations in this circuitry contribute to behavioural inflexibility observed in neuropsychiatric diseases including schizophrenia and autism.
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Affiliation(s)
- Josephine E Haddon
- Neuroscience and Mental Health Innovation Institute, Cardiff University, Cardiff, UK.
- Division of Psychological Medicine and Clinical Neurosciences (DPMCN), School of Medicine, Cardiff University, Cardiff, UK.
| | - Daniel Titherage
- School of Physiology, Pharmacology & Neuroscience, University of Bristol, Bristol, UK
- Centre for Academic Mental Health, Population Health sciences, University of Bristol, Bristol, UK
| | - Julia R Heckenast
- School of Physiology, Pharmacology & Neuroscience, University of Bristol, Bristol, UK
| | - Jennifer Carter
- Division of Psychological Medicine and Clinical Neurosciences (DPMCN), School of Medicine, Cardiff University, Cardiff, UK
| | - Michael J Owen
- Neuroscience and Mental Health Innovation Institute, Cardiff University, Cardiff, UK
- Division of Psychological Medicine and Clinical Neurosciences (DPMCN), School of Medicine, Cardiff University, Cardiff, UK
- MRC Centre for Neuropsychiatric Genetics and Genomics, Cardiff University, Cardiff, UK
| | - Jeremy Hall
- Neuroscience and Mental Health Innovation Institute, Cardiff University, Cardiff, UK
- Division of Psychological Medicine and Clinical Neurosciences (DPMCN), School of Medicine, Cardiff University, Cardiff, UK
- MRC Centre for Neuropsychiatric Genetics and Genomics, Cardiff University, Cardiff, UK
| | - Lawrence S Wilkinson
- Neuroscience and Mental Health Innovation Institute, Cardiff University, Cardiff, UK
- Division of Psychological Medicine and Clinical Neurosciences (DPMCN), School of Medicine, Cardiff University, Cardiff, UK
- MRC Centre for Neuropsychiatric Genetics and Genomics, Cardiff University, Cardiff, UK
- School of Psychology, Cardiff University, Cardiff, UK
| | - Matthew W Jones
- School of Physiology, Pharmacology & Neuroscience, University of Bristol, Bristol, UK
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11
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Wei GX, Shen H, Ge LK, Cao B, Manohar R, Zhang X. The altered volume of striatum: A neuroimaging marker of treatment in first-episode and drug-naïve schizophrenia. Schizophr Res Cogn 2024; 36:100308. [PMID: 38511167 PMCID: PMC10950692 DOI: 10.1016/j.scog.2024.100308] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/05/2023] [Revised: 02/20/2024] [Accepted: 03/04/2024] [Indexed: 03/22/2024]
Abstract
Although schizophrenia patients exhibit structural abnormalities in the striatum, it remains largely unknown for the role of the striatum subregions in the treatment response of antipsychotic drugs. The purpose of this study was to investigate the associations between the striatal subregions and improved clinical symptoms in first-episode drug-naïve (FEDN) schizophrenia. Forty-two FEDN schizophrenia patients and 29 healthy controls (HCs) were recruited. At baseline, the Positive and Negative Syndrome Scale (PANSS) was used to assess the clinical symptoms of patients, MRI scanner was used to obtain anatomical images of patients and HCs. After 12-week stable doses of risperidone treatment, clinical symptoms were obtained in 38 patients and anatomical images in 26 patients. After 12 weeks of treatment, the left nucleus accumbens volume decreased, whereas the left pallidum volume increased in schizophrenia patients. The decreased left nucleus accumbens volume was positively correlated with cognitive factor improvement measured by PANSS. Intriguingly, greater left nucleus accumbens volume at baseline predicted greater cognitive improvements. Furthermore, the responders who had >50 % improvement in cognitive symptoms exhibited significantly greater baseline left nucleus accumbens volume compared to non-responders. The left striatum volume at baseline and after treatment predicted the cognitive improvements in FEDN schizophrenia, which could be a potential biomarker for the development of precision medicine approaches targeting cognitive function.
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Affiliation(s)
- Gao-Xia Wei
- CAS Key Laboratory of Behavioral Science, Institute of Psychology, Beijing, China
- Department of Psychology, University of Chinese Academy of Sciences, Beijing, China
| | - Haoran Shen
- CAS Key Laboratory of Behavioral Science, Institute of Psychology, Beijing, China
- Department of Psychology, University of Chinese Academy of Sciences, Beijing, China
| | - Li-Kun Ge
- CAS Key Laboratory of Behavioral Science, Institute of Psychology, Beijing, China
- Department of Psychology, University of Chinese Academy of Sciences, Beijing, China
| | - Bo Cao
- Department of Psychiatry, University of Alberta, Edmonton, Canada
| | - Roja Manohar
- Health Science Center at Houston, University of Texas, USA
| | - Xiangyang Zhang
- CAS Key Laboratory of Mental Health, Institute of Psychology, Beijing, China
- Department of Psychology, University of Chinese Academy of Sciences, Beijing, China
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12
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Voineskos AN, Hawco C, Neufeld NH, Turner JA, Ameis SH, Anticevic A, Buchanan RW, Cadenhead K, Dazzan P, Dickie EW, Gallucci J, Lahti AC, Malhotra AK, Öngür D, Lencz T, Sarpal DK, Oliver LD. Functional magnetic resonance imaging in schizophrenia: current evidence, methodological advances, limitations and future directions. World Psychiatry 2024; 23:26-51. [PMID: 38214624 PMCID: PMC10786022 DOI: 10.1002/wps.21159] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 01/13/2024] Open
Abstract
Functional neuroimaging emerged with great promise and has provided fundamental insights into the neurobiology of schizophrenia. However, it has faced challenges and criticisms, most notably a lack of clinical translation. This paper provides a comprehensive review and critical summary of the literature on functional neuroimaging, in particular functional magnetic resonance imaging (fMRI), in schizophrenia. We begin by reviewing research on fMRI biomarkers in schizophrenia and the clinical high risk phase through a historical lens, moving from case-control regional brain activation to global connectivity and advanced analytical approaches, and more recent machine learning algorithms to identify predictive neuroimaging features. Findings from fMRI studies of negative symptoms as well as of neurocognitive and social cognitive deficits are then reviewed. Functional neural markers of these symptoms and deficits may represent promising treatment targets in schizophrenia. Next, we summarize fMRI research related to antipsychotic medication, psychotherapy and psychosocial interventions, and neurostimulation, including treatment response and resistance, therapeutic mechanisms, and treatment targeting. We also review the utility of fMRI and data-driven approaches to dissect the heterogeneity of schizophrenia, moving beyond case-control comparisons, as well as methodological considerations and advances, including consortia and precision fMRI. Lastly, limitations and future directions of research in the field are discussed. Our comprehensive review suggests that, in order for fMRI to be clinically useful in the care of patients with schizophrenia, research should address potentially actionable clinical decisions that are routine in schizophrenia treatment, such as which antipsychotic should be prescribed or whether a given patient is likely to have persistent functional impairment. The potential clinical utility of fMRI is influenced by and must be weighed against cost and accessibility factors. Future evaluations of the utility of fMRI in prognostic and treatment response studies may consider including a health economics analysis.
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Affiliation(s)
- Aristotle N Voineskos
- Campbell Family Mental Health Research Institute and Brain Health Imaging Centre, Centre for Addiction and Mental Health, Toronto, ON, Canada
- Department of Psychiatry, Temerty Faculty of Medicine, University of Toronto, Toronto, ON, Canada
| | - Colin Hawco
- Campbell Family Mental Health Research Institute and Brain Health Imaging Centre, Centre for Addiction and Mental Health, Toronto, ON, Canada
- Department of Psychiatry, Temerty Faculty of Medicine, University of Toronto, Toronto, ON, Canada
| | - Nicholas H Neufeld
- Campbell Family Mental Health Research Institute and Brain Health Imaging Centre, Centre for Addiction and Mental Health, Toronto, ON, Canada
- Department of Psychiatry, Temerty Faculty of Medicine, University of Toronto, Toronto, ON, Canada
| | - Jessica A Turner
- Department of Psychiatry and Behavioral Health, Wexner Medical Center, Ohio State University, Columbus, OH, USA
| | - Stephanie H Ameis
- Campbell Family Mental Health Research Institute and Brain Health Imaging Centre, Centre for Addiction and Mental Health, Toronto, ON, Canada
- Department of Psychiatry, Temerty Faculty of Medicine, University of Toronto, Toronto, ON, Canada
- Cundill Centre for Child and Youth Depression and McCain Centre for Child, Youth and Family Mental Health, Centre for Addiction and Mental Health, Toronto, ON, Canada
| | - Alan Anticevic
- Interdepartmental Neuroscience Program, Yale University, New Haven, CT, USA
- Department of Psychiatry, Yale University, New Haven, CT, USA
| | - Robert W Buchanan
- Maryland Psychiatric Research Center, Department of Psychiatry, University of Maryland School of Medicine, Baltimore, MD, USA
| | - Kristin Cadenhead
- Department of Psychiatry, University of California San Diego, La Jolla, CA, USA
| | - Paola Dazzan
- Department of Psychological Medicine, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, UK
| | - Erin W Dickie
- Campbell Family Mental Health Research Institute and Brain Health Imaging Centre, Centre for Addiction and Mental Health, Toronto, ON, Canada
- Department of Psychiatry, Temerty Faculty of Medicine, University of Toronto, Toronto, ON, Canada
| | - Julia Gallucci
- Campbell Family Mental Health Research Institute and Brain Health Imaging Centre, Centre for Addiction and Mental Health, Toronto, ON, Canada
- Institute of Medical Science, University of Toronto, Toronto, ON, Canada
| | - Adrienne C Lahti
- Department of Psychiatry and Behavioral Neurobiology, University of Alabama at Birmingham, Birmingham, AL, USA
| | - Anil K Malhotra
- Institute for Behavioral Science, Feinstein Institutes for Medical Research, Manhasset, NY, USA
- Department of Psychiatry, Zucker School of Medicine at Hofstra/Northwell, Hempstead, NY, USA
- Department of Molecular Medicine, Zucker School of Medicine at Hofstra/Northwell, Hempstead, NY, USA
- Department of Psychiatry, Zucker Hillside Hospital Division of Northwell Health, Glen Oaks, NY, USA
| | - Dost Öngür
- McLean Hospital/Harvard Medical School, Belmont, MA, USA
| | - Todd Lencz
- Institute for Behavioral Science, Feinstein Institutes for Medical Research, Manhasset, NY, USA
- Department of Psychiatry, Zucker School of Medicine at Hofstra/Northwell, Hempstead, NY, USA
- Department of Molecular Medicine, Zucker School of Medicine at Hofstra/Northwell, Hempstead, NY, USA
- Department of Psychiatry, Zucker Hillside Hospital Division of Northwell Health, Glen Oaks, NY, USA
| | - Deepak K Sarpal
- Department of Psychiatry, School of Medicine, University of Pittsburgh, Pittsburgh, PA, USA
| | - Lindsay D Oliver
- Campbell Family Mental Health Research Institute and Brain Health Imaging Centre, Centre for Addiction and Mental Health, Toronto, ON, Canada
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13
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Liang S, Zhao L, Ni P, Wang Q, Guo W, Xu Y, Cai J, Tao S, Li X, Deng W, Palaniyappan L, Li T. Frontostriatal circuitry and the tryptophan kynurenine pathway in major psychiatric disorders. Psychopharmacology (Berl) 2024; 241:97-107. [PMID: 37735237 DOI: 10.1007/s00213-023-06466-9] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/06/2023] [Accepted: 09/09/2023] [Indexed: 09/23/2023]
Abstract
RATIONALE An imbalance of the tryptophan kynurenine pathway (KP) commonly occurs in psychiatric disorders, though the neurocognitive and network-level effects of this aberration are unclear. OBJECTIVES In this study, we examined the connection between dysfunction in the frontostriatal brain circuits, imbalances in the tryptophan kynurenine pathway (KP), and neurocognition in major psychiatric disorders. METHODS Forty first-episode medication-naive patients with schizophrenia (SCZ), fifty patients with bipolar disorder (BD), fifty patients with major depressive disorder (MDD), and forty-two healthy controls underwent resting-state functional magnetic resonance imaging. Plasma levels of KP metabolites were measured, and neurocognitive function was evaluated. Frontostriatal connectivity and KP metabolites were compared between groups while controlling for demographic and clinical characteristics. Canonical correlation analyses were conducted to explore multidimensional relationships between frontostriatal circuits-KP and KP-cognitive features. RESULTS Patient groups shared hypoconnectivity between bilateral ventrolateral prefrontal cortex (vlPFC) and left insula, with disorder-specific dysconnectivity in SCZ related to PFC, left dorsal striatum hypoconnectivity. The BD group had higher anthranilic acid and lower xanthurenic acid levels than the other groups. KP metabolites and ratios related to disrupted frontostriatal dysconnectivity in a transdiagnostic manner. The SCZ group and MDD group separately had high-dimensional associations between KP metabolites and cognitive measures. CONCLUSIONS The findings suggest that KP may influence cognitive performance across psychiatric conditions via frontostriatal dysfunction.
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Affiliation(s)
- Sugai Liang
- Department of Neurobiology, Affiliated Mental Health Centre & Hangzhou Seventh People's Hospital, Zhejiang University School of Medicine, Hangzhou, 310013, Zhejiang, China
| | - Liansheng Zhao
- Mental Health Centre & Psychiatric Laboratory, State Key Laboratory of Biotherapy, West China Hospital, Sichuan University, Chengdu, 610041, Sichuan, China
| | - Peiyan Ni
- Mental Health Centre & Psychiatric Laboratory, State Key Laboratory of Biotherapy, West China Hospital, Sichuan University, Chengdu, 610041, Sichuan, China
| | - Qiang Wang
- Mental Health Centre & Psychiatric Laboratory, State Key Laboratory of Biotherapy, West China Hospital, Sichuan University, Chengdu, 610041, Sichuan, China
| | - Wanjun Guo
- Department of Neurobiology, Affiliated Mental Health Centre & Hangzhou Seventh People's Hospital, Zhejiang University School of Medicine, Hangzhou, 310013, Zhejiang, China
| | - Yan Xu
- Department of Neurobiology, Affiliated Mental Health Centre & Hangzhou Seventh People's Hospital, Zhejiang University School of Medicine, Hangzhou, 310013, Zhejiang, China
| | - Jia Cai
- Mental Health Centre & Psychiatric Laboratory, State Key Laboratory of Biotherapy, West China Hospital, Sichuan University, Chengdu, 610041, Sichuan, China
| | - Shiwan Tao
- Mental Health Centre & Psychiatric Laboratory, State Key Laboratory of Biotherapy, West China Hospital, Sichuan University, Chengdu, 610041, Sichuan, China
| | - Xiaojing Li
- Department of Neurobiology, Affiliated Mental Health Centre & Hangzhou Seventh People's Hospital, Zhejiang University School of Medicine, Hangzhou, 310013, Zhejiang, China
| | - Wei Deng
- Department of Neurobiology, Affiliated Mental Health Centre & Hangzhou Seventh People's Hospital, Zhejiang University School of Medicine, Hangzhou, 310013, Zhejiang, China
| | - Lena Palaniyappan
- Douglas Mental Health University Institute, Department of Psychiatry, McGill University, Montreal, Quebec, H4H1R3, Canada.
- Robarts Research Institute, Schulich School of Medicine and Dentistry, Western University, London, Ontario, N6A5K8, Canada.
| | - Tao Li
- Department of Neurobiology, Affiliated Mental Health Centre & Hangzhou Seventh People's Hospital, Zhejiang University School of Medicine, Hangzhou, 310013, Zhejiang, China.
- Liangzhu Laboratory, MOE Frontier Science Center for Brain Science and Brain-machine Integration, State Key Laboratory of Brain-machine Intelligence, Zhejiang University, Zhejiang, 310000, Hangzhou, China.
- NHC and CAMS Key Laboratory of Medical Neurobiology, Zhejiang University, Zhejiang, 310063, Hangzhou, China.
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14
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van Hooijdonk CFM, van der Pluijm M, de Vries BM, Cysouw M, Alizadeh BZ, Simons CJP, van Amelsvoort TAMJ, Booij J, Selten JP, de Haan L, Schirmbeck F, van de Giessen E. The association between clinical, sociodemographic, familial, and environmental factors and treatment resistance in schizophrenia: A machine-learning-based approach. Schizophr Res 2023; 262:132-141. [PMID: 37950936 DOI: 10.1016/j.schres.2023.10.030] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/20/2023] [Revised: 10/17/2023] [Accepted: 10/28/2023] [Indexed: 11/13/2023]
Abstract
BACKGROUND Prediction of treatment resistance in schizophrenia (TRS) would be helpful to reduce the duration of ineffective treatment and avoid delays in clozapine initiation. We applied machine learning to identify clinical, sociodemographic, familial, and environmental variables that are associated with TRS and could potentially predict TRS in the future. STUDY DESIGN Baseline and follow-up data on trait(-like) variables from the Genetic Risk and Outcome of Psychosis (GROUP) study were used. For the main analysis, we selected patients with non-affective psychotic disorders who met TRS (n = 200) or antipsychotic-responsive criteria (n = 423) throughout the study. For a sensitivity analysis, we only selected patients who met TRS (n = 76) or antipsychotic-responsive criteria (n = 123) at follow-up but not at baseline. Random forest models were trained to predict TRS in both datasets. SHapley Additive exPlanation values were used to examine the variables' contributions to the prediction. STUDY RESULTS Premorbid functioning, age at onset, and educational degree were most consistently associated with TRS across both analyses. Marital status, current household, intelligence quotient, number of moves, and family loading score for substance abuse also consistently contributed to the prediction of TRS in the main or sensitivity analysis. The diagnostic performance of our models was modest (area under the curve: 0.66-0.69). CONCLUSIONS We demonstrate that various clinical, sociodemographic, familial, and environmental variables are associated with TRS. Our models only showed modest performance in predicting TRS. Prospective large multi-centre studies are needed to validate our findings and investigate whether the model's performance can be improved by adding data from different modalities.
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Affiliation(s)
- Carmen F M van Hooijdonk
- Department of Psychiatry and Neuropsychology, School for Mental Health and Neuroscience (MHeNs), University of Maastricht, Maastricht, the Netherlands; Rivierduinen, Institute for Mental Health Care, Leiden, the Netherlands.
| | - Marieke van der Pluijm
- Department of Radiology and Nuclear Medicine, Amsterdam UMC, University of Amsterdam, the Netherlands; Department of Psychiatry, Amsterdam UMC, University of Amsterdam, the Netherlands
| | - Bart M de Vries
- Department of Radiology and Nuclear Medicine, Amsterdam UMC, University of Amsterdam, the Netherlands
| | - Matthijs Cysouw
- Department of Radiology and Nuclear Medicine, Amsterdam UMC, University of Amsterdam, the Netherlands
| | - Behrooz Z Alizadeh
- Rob Giel Research Center, University of Groningen, University Medical Center Groningen, University Center for Psychiatry, Groningen, the Netherlands; Department of Epidemiology, University Medical Center Groningen, Groningen, the Netherlands
| | - Claudia J P Simons
- Department of Psychiatry and Neuropsychology, School for Mental Health and Neuroscience (MHeNs), University of Maastricht, Maastricht, the Netherlands; GGzE, Institute for Mental Health Care, Eindhoven, the Netherlands
| | - Therese A M J van Amelsvoort
- Department of Psychiatry and Neuropsychology, School for Mental Health and Neuroscience (MHeNs), University of Maastricht, Maastricht, the Netherlands
| | - Jan Booij
- Department of Radiology and Nuclear Medicine, Amsterdam UMC, University of Amsterdam, the Netherlands
| | - Jean-Paul Selten
- Department of Psychiatry and Neuropsychology, School for Mental Health and Neuroscience (MHeNs), University of Maastricht, Maastricht, the Netherlands; Rivierduinen, Institute for Mental Health Care, Leiden, the Netherlands
| | - Lieuwe de Haan
- Department of Psychiatry, Amsterdam UMC, University of Amsterdam, the Netherlands
| | - Frederike Schirmbeck
- Department of Psychiatry, Amsterdam UMC, University of Amsterdam, the Netherlands
| | - Elsmarieke van de Giessen
- Department of Radiology and Nuclear Medicine, Amsterdam UMC, University of Amsterdam, the Netherlands; Amsterdam Neuroscience, Brain Imaging, Amsterdam, the Netherlands
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15
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Choi S, Kim M, Kim T, Choi EJ, Lee J, Moon SY, Cho SS, Lee J, Kwon JS. Fronto-striato-thalamic circuit connectivity and neuromelanin in schizophrenia: an fMRI and neuromelanin-MRI study. SCHIZOPHRENIA (HEIDELBERG, GERMANY) 2023; 9:81. [PMID: 37945576 PMCID: PMC10636101 DOI: 10.1038/s41537-023-00410-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/06/2023] [Accepted: 10/27/2023] [Indexed: 11/12/2023]
Abstract
Changes in dopamine and fronto-striato-thalamic (FST) circuit functional connectivity are prominent in schizophrenia. Dopamine is thought to underlie connectivity changes, but experimental evidence for this hypothesis is lacking. Previous studies examined the association in some of the connections using positron emission tomography (PET) and functional MRI (fMRI); however, PET has disadvantages in scanning patients, such as invasiveness. Excessive dopamine induces neuromelanin (NM) accumulation, and NM-MRI is suggested as a noninvasive proxy measure of dopamine function. We aimed to investigate the association between NM and FST circuit connectivity at the network level in patients with schizophrenia. We analysed substantia nigra NM-MRI and resting-state fMRI data from 29 schizophrenia patients and 63 age- and sex-matched healthy controls (HCs). We identified the FST subnetwork with abnormal connectivity found in schizophrenia patients compared to that of HCs and investigated the relationship between constituting connectivity and NM-MRI signal. We found a higher NM signal (t = -2.12, p = 0.037) and a hypoconnected FST subnetwork (FWER-corrected p = 0.014) in schizophrenia patients than in HCs. In the hypoconnected subnetwork of schizophrenia patients, lower left supplementary motor area-left caudate connectivity was associated with a higher NM signal (β = -0.38, p = 0.042). We demonstrated the association between NM and FST circuit connectivity. Considering that the NM-MRI signal reflects dopamine function, our results suggest that dopamine underlies changes in FST circuit connectivity, which supports the dopamine hypothesis. In addition, this study reveals implications for the future use of NM-MRI in investigations of the dopamine system.
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Affiliation(s)
- Sunah Choi
- Department of Brain and Cognitive Sciences, Seoul National University College of Natural Sciences, Seoul, Republic of Korea
| | - Minah Kim
- Department of Neuropsychiatry, Seoul National University Hospital, Seoul, Republic of Korea
- Department of Psychiatry, Seoul National University College of Medicine, Seoul, Republic of Korea
| | - Taekwan Kim
- Department of Bio and Brain Engineering, Korea Advanced Institute of Science and Technology, Daejeon, Republic of Korea
| | - Eun-Jung Choi
- Department of Electrical and Computer Engineering, Seoul National University, Seoul, Republic of Korea
| | - Jungha Lee
- Department of Brain and Cognitive Sciences, Seoul National University College of Natural Sciences, Seoul, Republic of Korea
| | - Sun-Young Moon
- Department of Neuropsychiatry, Seoul National University Hospital, Seoul, Republic of Korea
- Department of Psychiatry, Seoul National University College of Medicine, Seoul, Republic of Korea
- Department of Psychiatry, Seoul National University Bundang Hospital, Seongnam, Republic of Korea
| | - Sang Soo Cho
- Department of Brain and Cognitive Sciences, Seoul National University College of Natural Sciences, Seoul, Republic of Korea
| | - Jongho Lee
- Department of Electrical and Computer Engineering, Seoul National University, Seoul, Republic of Korea
| | - Jun Soo Kwon
- Department of Brain and Cognitive Sciences, Seoul National University College of Natural Sciences, Seoul, Republic of Korea.
- Department of Neuropsychiatry, Seoul National University Hospital, Seoul, Republic of Korea.
- Department of Psychiatry, Seoul National University College of Medicine, Seoul, Republic of Korea.
- Institute of Human Behavioral Medicine, SNU-MRC, Seoul, Republic of Korea.
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16
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Cao H, Lencz T, Gallego JA, Rubio JM, John M, Barber AD, Birnbaum ML, Robinson DG, Malhotra AK. A Functional Connectome-Based Neural Signature for Individualized Prediction of Antipsychotic Response in First-Episode Psychosis. Am J Psychiatry 2023; 180:827-835. [PMID: 37644811 PMCID: PMC11104773 DOI: 10.1176/appi.ajp.20220719] [Citation(s) in RCA: 6] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 08/31/2023]
Abstract
OBJECTIVE Identification of robust biomarkers that predict individualized response to antipsychotic treatment at the early stage of psychotic disorders remains a challenge in precision psychiatry. The aim of this study was to investigate whether any functional connectome-based neural traits could serve as such a biomarker. METHODS In a discovery sample, 49 patients with first-episode psychosis received multi-paradigm fMRI scans at baseline and were clinically followed up for 12 weeks under antipsychotic monotherapies. Treatment response was evaluated at the individual level based on the psychosis score of the Brief Psychiatric Rating Scale. Cross-paradigm connectivity and connectome-based predictive modeling were employed to train a predictive model that uses baseline connectomic measures to predict individualized change rates of psychosis scores, with model performance evaluated as the Pearson correlations between the predicted change rates and the observed change rates, based on cross-validation. The model generalizability was further examined in an independent validation sample of 24 patients in a similar design. RESULTS The results revealed a paradigm-independent connectomic trait that significantly predicted individualized treatment outcome in both the discovery sample (predicted-versus-observed r=0.41) and the validation sample (predicted-versus-observed r=0.47, mean squared error=0.019). Features that positively predicted psychosis change rates primarily involved connections related to the cerebellar-cortical circuitry, and features that negatively predicted psychosis change rates were chiefly connections within the cortical cognitive systems. CONCLUSIONS This study discovers and validates a connectome-based functional signature as a promising early predictor for individualized response to antipsychotic treatment in first-episode psychosis, thus highlighting the potential clinical value of this biomarker in precision psychiatry.
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Affiliation(s)
- Hengyi Cao
- Institute of Behavioral Sciences, Feinstein Institutes for Medical Research, Manhasset, N.Y. (all authors); Division of Psychiatry Research, Zucker Hillside Hospital, Glen Oaks, N.Y. (all authors); Department of Psychiatry, Zucker School of Medicine at Hofstra/Northwell, Hempstead, N.Y. (all authors)
| | - Todd Lencz
- Institute of Behavioral Sciences, Feinstein Institutes for Medical Research, Manhasset, N.Y. (all authors); Division of Psychiatry Research, Zucker Hillside Hospital, Glen Oaks, N.Y. (all authors); Department of Psychiatry, Zucker School of Medicine at Hofstra/Northwell, Hempstead, N.Y. (all authors)
| | - Juan A Gallego
- Institute of Behavioral Sciences, Feinstein Institutes for Medical Research, Manhasset, N.Y. (all authors); Division of Psychiatry Research, Zucker Hillside Hospital, Glen Oaks, N.Y. (all authors); Department of Psychiatry, Zucker School of Medicine at Hofstra/Northwell, Hempstead, N.Y. (all authors)
| | - Jose M Rubio
- Institute of Behavioral Sciences, Feinstein Institutes for Medical Research, Manhasset, N.Y. (all authors); Division of Psychiatry Research, Zucker Hillside Hospital, Glen Oaks, N.Y. (all authors); Department of Psychiatry, Zucker School of Medicine at Hofstra/Northwell, Hempstead, N.Y. (all authors)
| | - Majnu John
- Institute of Behavioral Sciences, Feinstein Institutes for Medical Research, Manhasset, N.Y. (all authors); Division of Psychiatry Research, Zucker Hillside Hospital, Glen Oaks, N.Y. (all authors); Department of Psychiatry, Zucker School of Medicine at Hofstra/Northwell, Hempstead, N.Y. (all authors)
| | - Anita D Barber
- Institute of Behavioral Sciences, Feinstein Institutes for Medical Research, Manhasset, N.Y. (all authors); Division of Psychiatry Research, Zucker Hillside Hospital, Glen Oaks, N.Y. (all authors); Department of Psychiatry, Zucker School of Medicine at Hofstra/Northwell, Hempstead, N.Y. (all authors)
| | - Michael L Birnbaum
- Institute of Behavioral Sciences, Feinstein Institutes for Medical Research, Manhasset, N.Y. (all authors); Division of Psychiatry Research, Zucker Hillside Hospital, Glen Oaks, N.Y. (all authors); Department of Psychiatry, Zucker School of Medicine at Hofstra/Northwell, Hempstead, N.Y. (all authors)
| | - Delbert G Robinson
- Institute of Behavioral Sciences, Feinstein Institutes for Medical Research, Manhasset, N.Y. (all authors); Division of Psychiatry Research, Zucker Hillside Hospital, Glen Oaks, N.Y. (all authors); Department of Psychiatry, Zucker School of Medicine at Hofstra/Northwell, Hempstead, N.Y. (all authors)
| | - Anil K Malhotra
- Institute of Behavioral Sciences, Feinstein Institutes for Medical Research, Manhasset, N.Y. (all authors); Division of Psychiatry Research, Zucker Hillside Hospital, Glen Oaks, N.Y. (all authors); Department of Psychiatry, Zucker School of Medicine at Hofstra/Northwell, Hempstead, N.Y. (all authors)
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17
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Dominicus LS, van Rijn L, van der A J, van der Spek R, Podzimek D, Begemann M, de Haan L, van der Pluijm M, Otte WM, Cahn W, Röder CH, Schnack HG, van Dellen E. fMRI connectivity as a biomarker of antipsychotic treatment response: A systematic review. Neuroimage Clin 2023; 40:103515. [PMID: 37797435 PMCID: PMC10568423 DOI: 10.1016/j.nicl.2023.103515] [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: 07/17/2023] [Revised: 08/31/2023] [Accepted: 09/22/2023] [Indexed: 10/07/2023]
Abstract
BACKGROUND Antipsychotic drugs are the first-choice therapy for psychotic episodes, but antipsychotic treatment response (AP-R) is unpredictable and only becomes clear after weeks of therapy. A biomarker for AP-R is currently unavailable. We reviewed the evidence for the hypothesis that functional magnetic resonance imaging functional connectivity (fMRI-FC) is a predictor of AP-R or could serve as a biomarker for AP-R in psychosis. METHOD A systematic review of longitudinal fMRI studies examining the predictive performance and relationship between FC and AP-R was performed following PRISMA guidelines. Technical and clinical aspects were critically assessed for the retrieved studies. We addressed three questions: Q1) is baseline fMRI-FC related to subsequent AP-R; Q2) is AP-R related to a change in fMRI-FC; and Q3) can baseline fMRI-FC predict subsequent AP-R? RESULTS In total, 28 articles were included. Most studies were of good quality. fMRI-FC analysis pipelines included seed-based-, independent component- / canonical correlation analysis, network-based statistics, and graph-theoretical approaches. We found high heterogeneity in methodological approaches and results. For Q1 (N = 17) and Q2 (N = 18), the most consistent evidence was found for FC between the striatum and ventral attention network as a potential biomarker of AP-R. For Q3 (N = 9) accuracy's varied form 50 till 93%, and prediction models were based on FC between various brain regions. CONCLUSION The current fMRI-FC literature on AP-R is hampered by heterogeneity of methodological approaches. Methodological uniformity and further improvement of the reliability and validity of fMRI connectivity analysis is needed before fMRI-FC analysis can have a place in clinical applications of antipsychotic treatment.
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Affiliation(s)
- L S Dominicus
- Department of Psychiatry, Brain Center, University Medical Center Utrecht, Utrecht, The Netherlands.
| | - L van Rijn
- Department of Psychiatry, Brain Center, University Medical Center Utrecht, Utrecht, The Netherlands
| | - J van der A
- Department of Psychiatry, Brain Center, University Medical Center Utrecht, Utrecht, The Netherlands
| | - R van der Spek
- Department of Psychiatry, Brain Center, University Medical Center Utrecht, Utrecht, The Netherlands
| | - D Podzimek
- Department of Psychiatry, Brain Center, University Medical Center Utrecht, Utrecht, The Netherlands
| | - M Begemann
- Department of Psychiatry, Brain Center, University Medical Center Utrecht, Utrecht, The Netherlands
| | - L de Haan
- Department Early Psychosis, Academical Medical Centre of the University of Amsterdam, Amsterdam, Amsterdam, The Netherlands
| | - M van der Pluijm
- Department of Radiology and Nuclear Medicine, Amsterdam UMC, University of Amsterdam, The Netherlands; Department of Psychiatry, Amsterdam UMC, University of Amsterdam, The Netherlands
| | - W M Otte
- Department of Child Neurology, UMC Utrecht Brain Center, University Medical Center Utrecht, and Utrecht University, Utrecht, The Netherlands
| | - W Cahn
- Department of Psychiatry, Brain Center, University Medical Center Utrecht, Utrecht, The Netherlands
| | - C H Röder
- Department of Psychiatry, Brain Center, University Medical Center Utrecht, Utrecht, The Netherlands
| | - H G Schnack
- Department of Psychiatry, Brain Center, University Medical Center Utrecht, Utrecht, The Netherlands
| | - E van Dellen
- Department of Psychiatry, Brain Center, University Medical Center Utrecht, Utrecht, The Netherlands; Department of Intensive Care Medicine and UMC Utrecht Brain Center, University Medical Center Utrecht, Utrecht University, Utrecht, The Netherlands
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18
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Niu X, Gao X, Zhang M, Dang J, Sun J, Lang Y, Wang W, Wei Y, Cheng J, Han S, Zhang Y. Static and dynamic changes of intrinsic brain local connectivity in internet gaming disorder. BMC Psychiatry 2023; 23:578. [PMID: 37558974 PMCID: PMC10410779 DOI: 10.1186/s12888-023-05009-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/13/2023] [Accepted: 07/07/2023] [Indexed: 08/11/2023] Open
Abstract
BACKGROUND Studies have revealed that intrinsic neural activity varies over time. However, the temporal variability of brain local connectivity in internet gaming disorder (IGD) remains unknown. The purpose of this study was to explore the alterations of static and dynamic intrinsic brain local connectivity in IGD and whether the changes were associated with clinical characteristics of IGD. METHODS Resting-state functional magnetic resonance imaging (rs-fMRI) scans were performed on 36 individuals with IGD (IGDs) and 44 healthy controls (HCs) matched for age, gender and years of education. The static regional homogeneity (sReHo) and dynamic ReHo (dReHo) were calculated and compared between two groups to detect the alterations of intrinsic brain local connectivity in IGD. The Internet Addiction Test (IAT) and the Pittsburgh Sleep Quality Index (PSQI) were used to evaluate the severity of online gaming addiction and sleep quality, respectively. Pearson correlation analysis was used to evaluate the relationship between brain regions with altered sReHo and dReHo and IAT and PSQI scores. Receiver operating characteristic (ROC) curve analysis was used to reveal the potential capacity of the sReHo and dReHo metrics to distinguish IGDs from HCs. RESULTS Compared with HCs, IGDs showed both increased static and dynamic intrinsic local connectivity in bilateral medial superior frontal gyrus (mSFG), superior frontal gyrus (SFG), and supplementary motor area (SMA). Increased dReHo in the left putamen, pallidum, caudate nucleus and bilateral thalamus were also observed. ROC curve analysis showed that the brain regions with altered sReHo and dReHo could distinguish individuals with IGD from HCs. Moreover, the sReHo values in the left mSFG and SMA as well as dReHo values in the left SMA were positively correlated with IAT scores. The dReHo values in the left caudate nucleus were negatively correlated with PSQI scores. CONCLUSIONS These results showed impaired intrinsic local connectivity in frontostriatothalamic circuitry in individuals with IGD, which may provide new insights into the underlying neuropathological mechanisms of IGD. Besides, dynamic changes of intrinsic local connectivity in caudate nucleus may be a potential neurobiological marker linking IGD and sleep quality.
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Affiliation(s)
- Xiaoyu Niu
- Department of Magnetic Resonance Imaging, First Affiliated Hospital of Zhengzhou University, Zhengzhou, Henan, China
- Key Laboratory for functional magnetic resonance imaging and molecular imaging of Henan Province, Henan Province, China
| | - Xinyu Gao
- Department of Magnetic Resonance Imaging, First Affiliated Hospital of Zhengzhou University, Zhengzhou, Henan, China
- Key Laboratory for functional magnetic resonance imaging and molecular imaging of Henan Province, Henan Province, China
| | - Mengzhe Zhang
- Department of Magnetic Resonance Imaging, First Affiliated Hospital of Zhengzhou University, Zhengzhou, Henan, China
- Key Laboratory for functional magnetic resonance imaging and molecular imaging of Henan Province, Henan Province, China
| | - Jinghan Dang
- Department of Magnetic Resonance Imaging, First Affiliated Hospital of Zhengzhou University, Zhengzhou, Henan, China
- Key Laboratory for functional magnetic resonance imaging and molecular imaging of Henan Province, Henan Province, China
| | - Jieping Sun
- Department of Magnetic Resonance Imaging, First Affiliated Hospital of Zhengzhou University, Zhengzhou, Henan, China
- Key Laboratory for functional magnetic resonance imaging and molecular imaging of Henan Province, Henan Province, China
| | - Yan Lang
- Department of Psychiatry, First Affiliated Hospital of Zhengzhou University, Zhengzhou, China
| | - Weijian Wang
- Department of Magnetic Resonance Imaging, First Affiliated Hospital of Zhengzhou University, Zhengzhou, Henan, China
- Key Laboratory for functional magnetic resonance imaging and molecular imaging of Henan Province, Henan Province, China
| | - Yarui Wei
- Department of Magnetic Resonance Imaging, First Affiliated Hospital of Zhengzhou University, Zhengzhou, Henan, China
- Key Laboratory for functional magnetic resonance imaging and molecular imaging of Henan Province, Henan Province, China
| | - Jingliang Cheng
- Department of Magnetic Resonance Imaging, First Affiliated Hospital of Zhengzhou University, Zhengzhou, Henan, China.
- Key Laboratory for functional magnetic resonance imaging and molecular imaging of Henan Province, Henan Province, China.
| | - Shaoqiang Han
- Department of Magnetic Resonance Imaging, First Affiliated Hospital of Zhengzhou University, Zhengzhou, Henan, China.
- Key Laboratory for functional magnetic resonance imaging and molecular imaging of Henan Province, Henan Province, China.
| | - Yong Zhang
- Department of Magnetic Resonance Imaging, First Affiliated Hospital of Zhengzhou University, Zhengzhou, Henan, China.
- Key Laboratory for functional magnetic resonance imaging and molecular imaging of Henan Province, Henan Province, China.
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19
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Selvaggi P, Jauhar S, Kotoula V, Pepper F, Veronese M, Santangelo B, Zelaya F, Turkheimer FE, Mehta MA, Howes OD. Reduced cortical cerebral blood flow in antipsychotic-free first-episode psychosis and relationship to treatment response. Psychol Med 2023; 53:5235-5245. [PMID: 36004510 PMCID: PMC10476071 DOI: 10.1017/s0033291722002288] [Citation(s) in RCA: 6] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/24/2021] [Revised: 06/27/2022] [Accepted: 07/04/2022] [Indexed: 11/07/2022]
Abstract
BACKGROUND Altered cerebral blood flow (CBF) has been found in people at risk for psychosis, with first-episode psychosis (FEP) and with chronic schizophrenia (SCZ). Studies using arterial spin labelling (ASL) have shown reduction of cortical CBF and increased subcortical CBF in SCZ. Previous studies have investigated CBF using ASL in FEP, reporting increased CBF in striatum and reduced CBF in frontal cortex. However, as these people were taking antipsychotics, it is unclear whether these changes are related to the disorder or antipsychotic treatment and how they relate to treatment response. METHODS We examined CBF in FEP free from antipsychotic medication (N = 21), compared to healthy controls (N = 22). Both absolute and relative-to-global CBF were assessed. We also investigated the association between baseline CBF and treatment response in a partially nested follow-up study (N = 14). RESULTS There was significantly lower absolute CBF in frontal cortex (Cohen's d = 0.84, p = 0.009) and no differences in striatum or hippocampus. Whole brain voxel-wise analysis revealed widespread cortical reductions in absolute CBF in large cortical clusters that encompassed occipital, parietal and frontal cortices (Threshold-Free Cluster Enhancement (TFCE)-corrected <0.05). No differences were found in relative-to-global CBF in the selected region of interests and in voxel-wise analysis. Relative-to-global frontal CBF was correlated with percentage change in total Positive and Negative Syndrome Scale after antipsychotic treatment (r = 0.67, p = 0.008). CONCLUSIONS These results show lower cortical absolute perfusion in FEP prior to starting antipsychotic treatment and suggest relative-to-global frontal CBF as assessed with magnetic resonance imaging could potentially serve as a biomarker for antipsychotic response.
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Affiliation(s)
- Pierluigi Selvaggi
- Department of Neuroimaging, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, UK
- Azienda Ospedaliero-Universitaria Consorziale Policlinico di Bari, Bari, Italy
| | - Sameer Jauhar
- Department of Psychosis Studies, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, UK
- Early Intervention Psychosis Clinical Academic Group, South London & Maudsley NHS Foundation Trust, London, UK
| | - Vasileia Kotoula
- Department of Neuroimaging, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, UK
| | - Fiona Pepper
- Department of Psychosis Studies, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, UK
| | - Mattia Veronese
- Department of Neuroimaging, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, UK
| | - Barbara Santangelo
- Department of Neuroimaging, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, UK
| | - Fernando Zelaya
- Department of Neuroimaging, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, UK
| | - Federico E. Turkheimer
- Department of Neuroimaging, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, UK
| | - Mitul A. Mehta
- Department of Neuroimaging, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, UK
| | - Oliver D. Howes
- Department of Psychosis Studies, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, UK
- MRC London Institute of Medical Sciences, Hammersmith Hospital, London W12 0NN, UK
- Faculty of Medicine, Institute of Clinical Sciences (ICS), Imperial College London, Du Cane Road, London W12 0NN, UK
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20
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Abi-Dargham A, Moeller SJ, Ali F, DeLorenzo C, Domschke K, Horga G, Jutla A, Kotov R, Paulus MP, Rubio JM, Sanacora G, Veenstra-VanderWeele J, Krystal JH. Candidate biomarkers in psychiatric disorders: state of the field. World Psychiatry 2023; 22:236-262. [PMID: 37159365 PMCID: PMC10168176 DOI: 10.1002/wps.21078] [Citation(s) in RCA: 57] [Impact Index Per Article: 57.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Accepted: 02/08/2023] [Indexed: 05/11/2023] Open
Abstract
The field of psychiatry is hampered by a lack of robust, reliable and valid biomarkers that can aid in objectively diagnosing patients and providing individualized treatment recommendations. Here we review and critically evaluate the evidence for the most promising biomarkers in the psychiatric neuroscience literature for autism spectrum disorder, schizophrenia, anxiety disorders and post-traumatic stress disorder, major depression and bipolar disorder, and substance use disorders. Candidate biomarkers reviewed include various neuroimaging, genetic, molecular and peripheral assays, for the purposes of determining susceptibility or presence of illness, and predicting treatment response or safety. This review highlights a critical gap in the biomarker validation process. An enormous societal investment over the past 50 years has identified numerous candidate biomarkers. However, to date, the overwhelming majority of these measures have not been proven sufficiently reliable, valid and useful to be adopted clinically. It is time to consider whether strategic investments might break this impasse, focusing on a limited number of promising candidates to advance through a process of definitive testing for a specific indication. Some promising candidates for definitive testing include the N170 signal, an event-related brain potential measured using electroencephalography, for subgroup identification within autism spectrum disorder; striatal resting-state functional magnetic resonance imaging (fMRI) measures, such as the striatal connectivity index (SCI) and the functional striatal abnormalities (FSA) index, for prediction of treatment response in schizophrenia; error-related negativity (ERN), an electrophysiological index, for prediction of first onset of generalized anxiety disorder, and resting-state and structural brain connectomic measures for prediction of treatment response in social anxiety disorder. Alternate forms of classification may be useful for conceptualizing and testing potential biomarkers. Collaborative efforts allowing the inclusion of biosystems beyond genetics and neuroimaging are needed, and online remote acquisition of selected measures in a naturalistic setting using mobile health tools may significantly advance the field. Setting specific benchmarks for well-defined target application, along with development of appropriate funding and partnership mechanisms, would also be crucial. Finally, it should never be forgotten that, for a biomarker to be actionable, it will need to be clinically predictive at the individual level and viable in clinical settings.
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Affiliation(s)
- Anissa Abi-Dargham
- Renaissance School of Medicine at Stony Brook University, Stony Brook, NY, USA
| | - Scott J Moeller
- Renaissance School of Medicine at Stony Brook University, Stony Brook, NY, USA
| | - Farzana Ali
- Renaissance School of Medicine at Stony Brook University, Stony Brook, NY, USA
| | - Christine DeLorenzo
- Renaissance School of Medicine at Stony Brook University, Stony Brook, NY, USA
| | - Katharina Domschke
- Department of Psychiatry and Psychotherapy, Faculty of Medicine, University of Freiburg, Freiburg, Germany
- Centre for Basics in Neuromodulation, Faculty of Medicine, University of Freiburg, Freiburg, Germany
| | - Guillermo Horga
- Department of Psychiatry, Columbia University, New York, NY, USA
- New York State Psychiatric Institute, New York, NY, USA
| | - Amandeep Jutla
- Department of Psychiatry, Columbia University, New York, NY, USA
- New York State Psychiatric Institute, New York, NY, USA
| | - Roman Kotov
- Renaissance School of Medicine at Stony Brook University, Stony Brook, NY, USA
| | | | - Jose M Rubio
- Zucker School of Medicine at Hofstra-Northwell, Hempstead, NY, USA
- Feinstein Institute for Medical Research - Northwell, Manhasset, NY, USA
- Zucker Hillside Hospital - Northwell Health, Glen Oaks, NY, USA
| | - Gerard Sanacora
- Department of Psychiatry, Yale School of Medicine, New Haven, CT, USA
| | - Jeremy Veenstra-VanderWeele
- Department of Psychiatry, Columbia University, New York, NY, USA
- New York State Psychiatric Institute, New York, NY, USA
| | - John H Krystal
- Department of Psychiatry, Yale School of Medicine, New Haven, CT, USA
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21
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Rubio JM, Guinart D, Kane JM, Correll CU. Early Non-Response to Antipsychotic Treatment in Schizophrenia: A Systematic Review and Meta-Analysis of Evidence-Based Management Options. CNS Drugs 2023; 37:499-512. [PMID: 37261669 DOI: 10.1007/s40263-023-01009-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Accepted: 05/02/2023] [Indexed: 06/02/2023]
Abstract
BACKGROUND Early non-response is a well-established prognostic marker but evidence-based and consistent recommendations to manage it are limited. The aim of this systematic review and meta-analysis was to generate evidence-based strategies for the management of schizophrenia patients with early non-response to 2 weeks of antipsychotic treatment. METHODS We conducted a systematic review and meta-analysis of randomized trials comparing antipsychotic dose escalation, switch, augmentation and continuation in individuals with study-defined early antipsychotic treatment non-response. Eligibility criteria were (1) clinical trials of primary psychosis treating for at least 2 weeks with antipsychotic monotherapy with study-defined operationalized criteria for early non-response; and (2) randomization to at least two of the following treatment strategies: dose escalation, switch, augmentation, or treatment continuation. Information sources were Pubmed, PsycINFO, and EMBASE, and risk of bias was assessed using Jadad scores. Results were synthesized using random-effects meta-analysis, comparing each intervention with treatment continuation for total symptom change as the primary outcome, generating standardized mean differences (SMDs) and 95% confidence intervals (CIs). Studies meeting the selection criteria but providing insufficient data for a meta-analysis were presented separately. RESULTS We screened 454 records by 1 August 2022, of which 12 individual datasets met the inclusion criteria, representing 947 research participants. Of those studies, five provided data to include in the meta-analysis (four with early non-response at 2 weeks, one at 3 weeks). Early non-response was defined within a timeline of 2 weeks in eight datasets, with the remaining datasets ranging between 3 and 4 weeks. The rates of early non-response ranged between 72.0 and 24.1%, and the endpoint ranged within 4-24 weeks post randomization. Quality was good (i.e., Jadad score of ≥3) in 8 of the 12 datasets. Overall, three studies compared antipsychotic switch versus continuation and two compared antipsychotic switch versus augmentation, in both cases without significant pooled between-group differences for total symptom severity (n = 149, SMD 0.18, 95% CI -0.14 to 0.5). Individually, two relatively large studies for antipsychotic switch versus continuation found small advantages for switching antipsychotics for total symptom severity (n = 149, SMD -0.49, 95% CI -1.05 to -0.06). One relatively large study found an advantage for dose escalation, although this finding has not been replicated and was not included in the meta-analysis. None of the alternatives included antipsychotic switch to clozapine. CONCLUSIONS Despite robust accuracy of early antipsychotic non-response predicting ultimate response, the evidence for treatment strategies that should be used for early non-response after 2-3 weeks is limited. While meta-analytic findings were non-significant, some individual studies suggest advantages of antipsychotic switch or dose escalation. Therefore, any conclusions should be interpreted carefully, given the insufficient high-quality evidence.
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Affiliation(s)
- Jose M Rubio
- The Zucker Hillside Hospital, Division of Psychiatry Research, Northwell Health, Glen Oaks, NY, USA
- Institute of Behavioral Science, The Feinstein Institute for Medical Research, Manhasset, NY, USA
- Zucker School of Medicine at Hofstra/Northwell, Hempstead, NY, USA
| | - Daniel Guinart
- The Zucker Hillside Hospital, Division of Psychiatry Research, Northwell Health, Glen Oaks, NY, USA
- Institute of Behavioral Science, The Feinstein Institute for Medical Research, Manhasset, NY, USA
- Zucker School of Medicine at Hofstra/Northwell, Hempstead, NY, USA
- Centro de Investigación Biomédica en Red de Salud Mental (CIBERSAM), Institut de Neuropsiquiatria i Addiccions (INAD), Hospital del Mar, Institut Hospital del Mar d'Investigacions Mèdiques (IMIM), Barcelona, Spain
| | - John M Kane
- The Zucker Hillside Hospital, Division of Psychiatry Research, Northwell Health, Glen Oaks, NY, USA
- Institute of Behavioral Science, The Feinstein Institute for Medical Research, Manhasset, NY, USA
- Zucker School of Medicine at Hofstra/Northwell, Hempstead, NY, USA
| | - Christoph U Correll
- The Zucker Hillside Hospital, Division of Psychiatry Research, Northwell Health, Glen Oaks, NY, USA.
- Institute of Behavioral Science, The Feinstein Institute for Medical Research, Manhasset, NY, USA.
- Zucker School of Medicine at Hofstra/Northwell, Hempstead, NY, USA.
- Department of Child and Adolescent Psychiatry, Charité-Universitätsmedizin Berlin, Berlin, Germany.
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22
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Dhamala E, Yeo BTT, Holmes AJ. One Size Does Not Fit All: Methodological Considerations for Brain-Based Predictive Modeling in Psychiatry. Biol Psychiatry 2023; 93:717-728. [PMID: 36577634 DOI: 10.1016/j.biopsych.2022.09.024] [Citation(s) in RCA: 19] [Impact Index Per Article: 19.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/13/2022] [Revised: 09/07/2022] [Accepted: 09/23/2022] [Indexed: 12/30/2022]
Abstract
Psychiatric illnesses are heterogeneous in nature. No illness manifests in the same way across individuals, and no two patients with a shared diagnosis exhibit identical symptom profiles. Over the last several decades, group-level analyses of in vivo neuroimaging data have led to fundamental advances in our understanding of the neurobiology of psychiatric illnesses. More recently, access to computational resources and large, publicly available datasets alongside the rise of predictive modeling and precision medicine approaches have facilitated the study of psychiatric illnesses at an individual level. Data-driven machine learning analyses can be applied to identify disease-relevant biological subtypes, predict individual symptom profiles, and recommend personalized therapeutic interventions. However, when developing these predictive models, methodological choices must be carefully considered to ensure accurate, robust, and interpretable results. Choices pertaining to algorithms, neuroimaging modalities and states, data transformation, phenotypes, parcellations, sample sizes, and populations we are specifically studying can influence model performance. Here, we review applications of neuroimaging-based machine learning models to study psychiatric illnesses and discuss the effects of different methodological choices on model performance. An understanding of these effects is crucial for the proper implementation of predictive models in psychiatry and will facilitate more accurate diagnoses, prognoses, and therapeutics.
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Affiliation(s)
- Elvisha Dhamala
- Department of Psychology, Yale University, New Haven, Connecticut; Kavli Institute for Neuroscience, Yale University, New Haven, Connecticut.
| | - B T Thomas Yeo
- Centre for Sleep & Cognition & Centre for Translational Magnetic Resonance Research, Yong Loo Lin School of Medicine, Singapore, National University of Singapore, Singapore; Department of Electrical and Computer Engineering, National University of Singapore, Singapore; N.1 Institute for Health & Institute for Digital Medicine, National University of Singapore, Singapore; Integrative Sciences and Engineering Programme, National University of Singapore, Singapore; Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Charlestown, Massachusetts
| | - Avram J Holmes
- Department of Psychology, Yale University, New Haven, Connecticut; Kavli Institute for Neuroscience, Yale University, New Haven, Connecticut; Department of Psychiatry, Yale University, New Haven, Connecticut; Wu Tsai Institute, Yale University, New Haven, Connecticut.
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23
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Hancock F, Rosas FE, McCutcheon RA, Cabral J, Dipasquale O, Turkheimer FE. Metastability as a candidate neuromechanistic biomarker of schizophrenia pathology. PLoS One 2023; 18:e0282707. [PMID: 36952467 PMCID: PMC10035891 DOI: 10.1371/journal.pone.0282707] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/14/2022] [Accepted: 02/21/2023] [Indexed: 03/25/2023] Open
Abstract
The disconnection hypothesis of schizophrenia proposes that symptoms of the disorder arise as a result of aberrant functional integration between segregated areas of the brain. The concept of metastability characterizes the coexistence of competing tendencies for functional integration and functional segregation in the brain, and is therefore well suited for the study of schizophrenia. In this study, we investigate metastability as a candidate neuromechanistic biomarker of schizophrenia pathology, including a demonstration of reliability and face validity. Group-level discrimination, individual-level classification, pathophysiological relevance, and explanatory power were assessed using two independent case-control studies of schizophrenia, the Human Connectome Project Early Psychosis (HCPEP) study (controls n = 53, non-affective psychosis n = 82) and the Cobre study (controls n = 71, cases n = 59). In this work we extend Leading Eigenvector Dynamic Analysis (LEiDA) to capture specific features of dynamic functional connectivity and then implement a novel approach to estimate metastability. We used non-parametric testing to evaluate group-level differences and a naïve Bayes classifier to discriminate cases from controls. Our results show that our new approach is capable of discriminating cases from controls with elevated effect sizes relative to published literature, reflected in an up to 76% area under the curve (AUC) in out-of-sample classification analyses. Additionally, our new metric showed explanatory power of between 81-92% for measures of integration and segregation. Furthermore, our analyses demonstrated that patients with early psychosis exhibit intermittent disconnectivity of subcortical regions with frontal cortex and cerebellar regions, introducing new insights about the mechanistic bases of these conditions. Overall, these findings demonstrate reliability and face validity of metastability as a candidate neuromechanistic biomarker of schizophrenia pathology.
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Affiliation(s)
- Fran Hancock
- Department of Neuroimaging, Institute of Psychiatry, Psychology and Neuroscience, King’s College London, De Crespigny Park, London, United Kingdom
| | - Fernando E. Rosas
- Department of Informatics, University of Sussex, Brighton, United Kingdom
- Centre for Psychedelic Research, Department of Brain Science, Imperial College London, London, United Kingdom
- Centre for Complexity Science, Imperial College London, London, United Kingdom
- Centre for Eudaimonia and Human Flourishing, University of Oxford, Oxford, United Kingdom
| | - Robert A. McCutcheon
- Department of Psychosis Studies, Institute of Psychiatry, Psychology & Neuroscience, King’s College London, De Crespigny Park, London, United Kingdom
- Department of Psychiatry, University of Oxford, Oxford, United Kingdom
| | - Joana Cabral
- Department of Psychiatry, University of Oxford, Oxford, United Kingdom
- Life and Health Sciences Research Institute School of Medicine, University of Minho, Braga, Portugal
| | - Ottavia Dipasquale
- Department of Neuroimaging, Institute of Psychiatry, Psychology and Neuroscience, King’s College London, De Crespigny Park, London, United Kingdom
| | - Federico E. Turkheimer
- Department of Neuroimaging, Institute of Psychiatry, Psychology and Neuroscience, King’s College London, De Crespigny Park, London, United Kingdom
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24
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Zong X, Wu K, Li L, Zhang J, Ma S, Kang L, Zhang N, Lv L, Sang D, Weng S, Chen H, Zheng J, Hu M. Striatum-related spontaneous coactivation patterns predict treatment response on positive symptoms of drug-naive first-episode schizophrenia with risperidone monotherapy. Front Psychiatry 2023; 14:1093030. [PMID: 37009110 PMCID: PMC10050338 DOI: 10.3389/fpsyt.2023.1093030] [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: 11/08/2022] [Accepted: 03/03/2023] [Indexed: 03/17/2023] Open
Abstract
BackgroundEvidence from functional magnetic resonance imaging (fMRI) studies of schizophrenia suggests that interindividual variation in the stationary striatal functional circuit may be correlated with antipsychotic treatment response. However, little is known about the role of the dynamic striatum-related network in predicting patients’ clinical improvement. The spontaneous coactivation pattern (CAP) technique has recently been found to be important for elucidating the non-stationary nature of functional brain networks.MethodsForty-two drug-naive first-episode schizophrenia patients underwent fMRI and T1W imaging before and after 8 weeks of risperidone monotherapy. The striatum was divided into 3 subregions, including the putamen, pallidum, and caudate. Spontaneous CAPs and CAP states were utilized to measure the dynamic characteristics of brain networks. We used DPARSF and Dynamic Brain Connectome software to analyze each subregion-related CAP and CAP state for each group and then compared the between-group differences in the neural network biomarkers. We used Pearson’s correlation analysis to determine the associations between the neuroimaging measurements with between-group differences and the improvement in patients’ psychopathological symptoms.ResultsIn the putamen-related CAPs, patients showed significantly increased intensity in the bilateral thalamus, bilateral supplementary motor areas, bilateral medial, and paracingulate gyrus, left paracentral lobule, left medial superior frontal gyrus, and left anterior cingulate gyrus compared with healthy controls. After treatment, thalamic signals in the putamen-related CAP 1 showed a significant increase, while the signals of the medial and paracingulate gyrus in the putamen-related CAP 3 revealed a significant decrease. The increase in thalamic signal intensity in the putamen-related CAP 1 was significantly and positively correlated with the percentage reduction in PANSS_P.ConclusionThis study is the first to combine striatal CAPs and fMRI to explore treatment response-related biomarkers in the early phase of schizophrenia. Our findings suggest that dynamic changes in CAP states in the putamen-thalamus circuit may be potential biomarkers for predicting patients’ variation in the short-term treatment response of positive symptoms.
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Affiliation(s)
- Xiaofen Zong
- Department of Psychiatry, Renmin Hospital of Wuhan University, Wuhan, China
| | - Kai Wu
- High-Field Magnetic Resonance Brain Imaging Key Laboratory of Sichuan Province, School of Life Sciences and Technology, University of Electronic Science and Technology of China, Chengdu, China
| | - Lei Li
- High-Field Magnetic Resonance Brain Imaging Key Laboratory of Sichuan Province, School of Life Sciences and Technology, University of Electronic Science and Technology of China, Chengdu, China
| | - Jiangbo Zhang
- High-Field Magnetic Resonance Brain Imaging Key Laboratory of Sichuan Province, School of Life Sciences and Technology, University of Electronic Science and Technology of China, Chengdu, China
| | - Simeng Ma
- Department of Psychiatry, Renmin Hospital of Wuhan University, Wuhan, China
| | - Lijun Kang
- Department of Psychiatry, Renmin Hospital of Wuhan University, Wuhan, China
| | - Nan Zhang
- Department of Psychiatry, Renmin Hospital of Wuhan University, Wuhan, China
| | - Luxian Lv
- Department of Psychiatry, Henan Mental Hospital, The Second Affiliated Hospital of Xinxiang Medical University, Xinxiang, China
| | - Deen Sang
- Department of Psychiatry, Henan Mental Hospital, The Second Affiliated Hospital of Xinxiang Medical University, Xinxiang, China
| | - Shenhong Weng
- Department of Psychiatry, Renmin Hospital of Wuhan University, Wuhan, China
- Shenhong Weng,
| | - Huafu Chen
- High-Field Magnetic Resonance Brain Imaging Key Laboratory of Sichuan Province, School of Life Sciences and Technology, University of Electronic Science and Technology of China, Chengdu, China
- Huafu Chen,
| | - Junjie Zheng
- Early Intervention Unit, Department of Psychiatry, Affiliated Nanjing Brain Hospital, Nanjing Medical University, Nanjing, China
- Functional Brain Imaging Institute of Nanjing Medical University, Nanjing, China
- Junjie Zheng,
| | - Maolin Hu
- Department of Psychiatry, Renmin Hospital of Wuhan University, Wuhan, China
- *Correspondence: Maolin Hu,
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25
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Ji JL, Lencz T, Gallego J, Neufeld N, Voineskos A, Malhotra A, Anticevic A. Informing individualized multi-scale neural signatures of clozapine response in patients with treatment-refractory schizophrenia. MEDRXIV : THE PREPRINT SERVER FOR HEALTH SCIENCES 2023:2023.03.10.23286854. [PMID: 36993630 PMCID: PMC10055439 DOI: 10.1101/2023.03.10.23286854] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/19/2023]
Abstract
Clozapine is currently the only antipsychotic with demonstrated efficacy in treatment-refractory schizophrenia (TRS). However, response to clozapine differs widely between TRS patients, and there are no available clinical or neural predictive indicators that could be used to increase or accelerate the use of clozapine in patients who stand to benefit. Furthermore, it remains unclear how the neuropharmacology of clozapine contributes to its therapeutic effects. Identifying the mechanisms underlying clozapine's therapeutic effects across domains of symptomatology could be crucial for development of new optimized therapies for TRS. Here, we present results from a prospective neuroimaging study that quantitatively related heterogeneous patterns of clinical clozapine response to neural functional connectivity at baseline. We show that we can reliably capture specific dimensions of clozapine clinical response by quantifying the full variation across item-level clinical scales, and that these dimensions can be mapped to neural features that are sensitive to clozapine-induced symptom change. Thus, these features may act as "failure modes" that can provide an early indication of treatment (non-)responsiveness. Lastly, we related the response-relevant neural maps to spatial expression profiles of genes coding for receptors implicated in clozapine's pharmacology, demonstrating that distinct dimensions of clozapine symptom-informed neural features may be associated with specific receptor targets. Collectively, this study informs prognostic neuro-behavioral measures for clozapine as a more optimal treatment for selected patients with TRS. We provide support for the identification of neuro-behavioral targets linked to pharmacological efficacy that can be further developed to inform optimal early treatment decisions in schizophrenia.
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26
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Chuang KC, Ramakrishnapillai S, Madden K, St Amant J, McKlveen K, Gwizdala K, Dhullipudi R, Bazzano L, Carmichael O. Brain effective connectivity and functional connectivity as markers of lifespan vascular exposures in middle-aged adults: The Bogalusa Heart Study. Front Aging Neurosci 2023; 15:1110434. [PMID: 36998317 PMCID: PMC10043334 DOI: 10.3389/fnagi.2023.1110434] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/28/2022] [Accepted: 02/22/2023] [Indexed: 03/16/2023] Open
Abstract
IntroductionEffective connectivity (EC), the causal influence that functional activity in a source brain location exerts over functional activity in a target brain location, has the potential to provide different information about brain network dynamics than functional connectivity (FC), which quantifies activity synchrony between locations. However, head-to-head comparisons between EC and FC from either task-based or resting-state functional MRI (fMRI) data are rare, especially in terms of how they associate with salient aspects of brain health.MethodsIn this study, 100 cognitively-healthy participants in the Bogalusa Heart Study aged 54.2 ± 4.3years completed Stroop task-based fMRI, resting-state fMRI. EC and FC among 24 regions of interest (ROIs) previously identified as involved in Stroop task execution (EC-task and FC-task) and among 33 default mode network ROIs (EC-rest and FC-rest) were calculated from task-based and resting-state fMRI using deep stacking networks and Pearson correlation. The EC and FC measures were thresholded to generate directed and undirected graphs, from which standard graph metrics were calculated. Linear regression models related graph metrics to demographic, cardiometabolic risk factors, and cognitive function measures.ResultsWomen and whites (compared to men and African Americans) had better EC-task metrics, and better EC-task metrics associated with lower blood pressure, white matter hyperintensity volume, and higher vocabulary score (maximum value of p = 0.043). Women had better FC-task metrics, and better FC-task metrics associated with APOE-ε4 3–3 genotype and better hemoglobin-A1c, white matter hyperintensity volume and digit span backwards score (maximum value of p = 0.047). Better EC rest metrics associated with lower age, non-drinker status, and better BMI, white matter hyperintensity volume, logical memory II total score, and word reading score (maximum value of p = 0.044). Women and non-drinkers had better FC-rest metrics (value of p = 0.004).DiscussionIn a diverse, cognitively healthy, middle-aged community sample, EC and FC based graph metrics from task-based fMRI data, and EC based graph metrics from resting-state fMRI data, were associated with recognized indicators of brain health in differing ways. Future studies of brain health should consider taking both task-based and resting-state fMRI scans and measuring both EC and FC analyses to get a more complete picture of functional networks relevant to brain health.
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Affiliation(s)
- Kai-Cheng Chuang
- Department of Physics & Astronomy, Louisiana State University, Baton Rouge, LA, United States
- Pennington Biomedical Research Center, Baton Rouge, LA, United States
- *Correspondence: Kai-Cheng Chuang,
| | - Sreekrishna Ramakrishnapillai
- Pennington Biomedical Research Center, Baton Rouge, LA, United States
- Department of Electrical and Computer Engineering, Louisiana State University, Baton Rouge, LA, United States
| | - Kaitlyn Madden
- Pennington Biomedical Research Center, Baton Rouge, LA, United States
| | - Julia St Amant
- Pennington Biomedical Research Center, Baton Rouge, LA, United States
| | - Kevin McKlveen
- Pennington Biomedical Research Center, Baton Rouge, LA, United States
| | - Kathryn Gwizdala
- Pennington Biomedical Research Center, Baton Rouge, LA, United States
| | | | - Lydia Bazzano
- Department of Epidemiology, Tulane University School of Public Health and Tropical Medicine, New Orleans, LA, United States
| | - Owen Carmichael
- Pennington Biomedical Research Center, Baton Rouge, LA, United States
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27
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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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28
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Machine learning methods to predict outcomes of pharmacological treatment in psychosis. Transl Psychiatry 2023; 13:75. [PMID: 36864017 PMCID: PMC9981732 DOI: 10.1038/s41398-023-02371-z] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/14/2022] [Revised: 02/01/2023] [Accepted: 02/14/2023] [Indexed: 03/04/2023] Open
Abstract
In recent years, machine learning (ML) has been a promising approach in the research of treatment outcome prediction in psychosis. In this study, we reviewed ML studies using different neuroimaging, neurophysiological, genetic, and clinical features to predict antipsychotic treatment outcomes in patients at different stages of schizophrenia. Literature available on PubMed until March 2022 was reviewed. Overall, 28 studies were included, among them 23 using a single-modality approach and 5 combining data from multiple modalities. The majority of included studies considered structural and functional neuroimaging biomarkers as predictive features used in ML models. Specifically, functional magnetic resonance imaging (fMRI) features contributed to antipsychotic treatment response prediction of psychosis with good accuracies. Additionally, several studies found that ML models based on clinical features might present adequate predictive ability. Importantly, by examining the additive effects of combining features, the predictive value might be improved by applying multimodal ML approaches. However, most of the included studies presented several limitations, such as small sample sizes and a lack of replication tests. Moreover, considerable clinical and analytical heterogeneity among included studies posed a challenge in synthesizing findings and generating robust overall conclusions. Despite the complexity and heterogeneity of methodology, prognostic features, clinical presentation, and treatment approaches, studies included in this review suggest that ML tools may have the potential to predict treatment outcomes of psychosis accurately. Future studies need to focus on refining feature characterization, validating prediction models, and evaluate their translation in real-world clinical practice.
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29
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Kim M, Kim T, Hwang WJ, Lho SK, Moon SY, Lee TY, Kwon JS. Forecasting prognostic trajectories with mismatch negativity in early psychosis. Psychol Med 2023; 53:1489-1499. [PMID: 36315242 PMCID: PMC10009395 DOI: 10.1017/s0033291721003068] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/10/2021] [Revised: 07/01/2021] [Accepted: 07/14/2021] [Indexed: 11/06/2022]
Abstract
BACKGROUND Prognostic heterogeneity in early psychosis patients yields significant difficulties in determining the degree and duration of early intervention; this heterogeneity highlights the need for prognostic biomarkers. Although mismatch negativity (MMN) has been widely studied across early phases of psychotic disorders, its potential as a common prognostic biomarker in early periods, such as clinical high risk (CHR) for psychosis and first-episode psychosis (FEP), has not been fully studied. METHODS A total of 104 FEP patients, 102 CHR individuals, and 107 healthy controls (HCs) participated in baseline MMN recording. Clinical outcomes were assessed; 17 FEP patients were treatment resistant, 73 FEP patients were nonresistant, 56 CHR individuals were nonremitters (15 transitioned to a psychotic disorder), and 22 CHR subjects were remitters. Baseline MMN amplitudes were compared across clinical outcome groups and tested for utility prognostic biomarkers using binary logistic regression. RESULTS MMN amplitudes were greatest in HCs, intermediate in CHR subjects, and smallest in FEP patients. In the clinical outcome groups, MMN amplitudes were reduced from the baseline in both FEP and CHR patients with poor prognostic trajectories. Reduced baseline MMN amplitudes were a significant predictor of later treatment resistance in FEP patients [Exp(β) = 2.100, 95% confidence interval (CI) 1.104-3.993, p = 0.024] and nonremission in CHR individuals [Exp(β) = 1.898, 95% CI 1.065-3.374, p = 0.030]. CONCLUSIONS These findings suggest that MMN could be used as a common prognostic biomarker across early psychosis periods, which will aid clinical decisions for early intervention.
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Affiliation(s)
- Minah Kim
- Department of Neuropsychiatry, Seoul National University Hospital, Seoul, Republic of Korea
- Department of Psychiatry, Seoul National University College of Medicine, Seoul, Republic of Korea
| | - Taekwan Kim
- Department of Brain and Cognitive Sciences, Seoul National University College of Natural Sciences, Seoul, Republic of Korea
| | - Wu Jeong Hwang
- Department of Brain and Cognitive Sciences, Seoul National University College of Natural Sciences, Seoul, Republic of Korea
| | - Silvia Kyungjin Lho
- Department of Neuropsychiatry, Seoul National University Hospital, Seoul, Republic of Korea
- Department of Psychiatry, Seoul National University College of Medicine, Seoul, Republic of Korea
| | - Sun-Young Moon
- Department of Neuropsychiatry, Seoul National University Hospital, Seoul, Republic of Korea
- Department of Psychiatry, Seoul National University College of Medicine, Seoul, Republic of Korea
| | - Tae Young Lee
- Department of Neuropsychiatry, Pusan National University Yangsan Hospital, Yangsan, Republic of Korea
- Institute of Human Behavioral Medicine, SNU-MRC, Seoul, Republic of Korea
| | - Jun Soo Kwon
- Department of Neuropsychiatry, Seoul National University Hospital, Seoul, Republic of Korea
- 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
- Institute of Human Behavioral Medicine, SNU-MRC, Seoul, Republic of Korea
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Egerton A, Griffiths K, Casetta C, Deakin B, Drake R, Howes OD, Kassoumeri L, Khan S, Lankshear S, Lees J, Lewis S, Mikulskaya E, Millgate E, Oloyede E, Pollard R, Rich N, Segev A, Sendt KV, MacCabe JH. Anterior cingulate glutamate metabolites as a predictor of antipsychotic response in first episode psychosis: data from the STRATA collaboration. Neuropsychopharmacology 2023; 48:567-575. [PMID: 36456813 PMCID: PMC9852590 DOI: 10.1038/s41386-022-01508-w] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/13/2022] [Revised: 11/07/2022] [Accepted: 11/09/2022] [Indexed: 12/03/2022]
Abstract
Elevated brain glutamate has been implicated in non-response to antipsychotic medication in schizophrenia. Biomarkers that can accurately predict antipsychotic non-response from the first episode of psychosis (FEP) could allow stratification of patients; for example, patients predicted not to respond to standard antipsychotics could be fast-tracked to clozapine. Using proton magnetic resonance spectroscopy (1H-MRS), we examined the ability of glutamate and Glx (glutamate plus glutamine) in the anterior cingulate cortex (ACC) and caudate to predict response to antipsychotic treatment. A total of 89 minimally medicated patients with FEP not meeting symptomatic criteria for remission were recruited across two study sites. 1H-MRS and clinical data were acquired at baseline, 2 and 6 weeks. Response was defined as >20% reduction in Positive and Negative Syndrome Scale (PANSS) Total score from baseline to 6 weeks. In the ACC, baseline glutamate and Glx were higher in Non-Responders and significantly predicted response (P < 0.02; n = 42). Overall accuracy was greatest for ACC Glx (69%) and increased to 75% when symptom severity at baseline was included in the model. Glutamate metabolites in the caudate were not associated with response, and there was no significant change in glutamate metabolites over time in either region. These results add to the evidence linking elevations in ACC glutamate metabolites to a poor antipsychotic response. They indicate that glutamate may have utility in predicting response during early treatment of first episode psychosis. Improvements in accuracy may be made by combining glutamate measures with other response biomarkers.
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Affiliation(s)
- Alice Egerton
- Department of Psychosis Studies, Institute of Psychiatry, Psychology & Neuroscience, King's College London, London, UK.
- NIHR Biomedical Research Centre at South London and Maudsley NHS Foundation Trust, London, UK.
| | - Kira Griffiths
- Department of Psychosis Studies, Institute of Psychiatry, Psychology & Neuroscience, King's College London, London, UK
| | - Cecila Casetta
- Department of Psychosis Studies, Institute of Psychiatry, Psychology & Neuroscience, King's College London, London, UK
| | - Bill Deakin
- Division of Neuroscience and Experimental Psychology, School of Biological Sciences, Faculty of Biology, Medicine and Health, University of Manchester, Manchester, UK
- Greater Manchester Mental Health NHS Foundation Trust Bury New Road, Prestwich, Manchester, M25 3BL, UK
| | - Richard Drake
- Greater Manchester Mental Health NHS Foundation Trust Bury New Road, Prestwich, Manchester, M25 3BL, UK
- Division of Psychology and Mental Health, School of Biological Sciences, Faculty of Biology, Medicine and Health, University of Manchester, Manchester, UK
| | - Oliver D Howes
- Department of Psychosis Studies, Institute of Psychiatry, Psychology & Neuroscience, King's College London, London, UK
- NIHR Biomedical Research Centre at South London and Maudsley NHS Foundation Trust, London, UK
| | - Laura Kassoumeri
- Department of Psychosis Studies, Institute of Psychiatry, Psychology & Neuroscience, King's College London, London, UK
| | - Sobia Khan
- Greater Manchester Mental Health NHS Foundation Trust Bury New Road, Prestwich, Manchester, M25 3BL, UK
- Division of Psychology and Mental Health, School of Biological Sciences, Faculty of Biology, Medicine and Health, University of Manchester, Manchester, UK
| | - Steve Lankshear
- Greater Manchester Mental Health NHS Foundation Trust Bury New Road, Prestwich, Manchester, M25 3BL, UK
- Division of Psychology and Mental Health, School of Biological Sciences, Faculty of Biology, Medicine and Health, University of Manchester, Manchester, UK
| | - Jane Lees
- Division of Psychology and Mental Health, School of Biological Sciences, Faculty of Biology, Medicine and Health, University of Manchester, Manchester, UK
| | - Shon Lewis
- Greater Manchester Mental Health NHS Foundation Trust Bury New Road, Prestwich, Manchester, M25 3BL, UK
- Division of Psychology and Mental Health, School of Biological Sciences, Faculty of Biology, Medicine and Health, University of Manchester, Manchester, UK
| | - Elena Mikulskaya
- Greater Manchester Mental Health NHS Foundation Trust Bury New Road, Prestwich, Manchester, M25 3BL, UK
- Division of Psychology and Mental Health, School of Biological Sciences, Faculty of Biology, Medicine and Health, University of Manchester, Manchester, UK
| | - Edward Millgate
- Department of Psychosis Studies, Institute of Psychiatry, Psychology & Neuroscience, King's College London, London, UK
| | - Ebenezer Oloyede
- Department of Psychosis Studies, Institute of Psychiatry, Psychology & Neuroscience, King's College London, London, UK
| | - Rebecca Pollard
- Department of Psychosis Studies, Institute of Psychiatry, Psychology & Neuroscience, King's College London, London, UK
| | - Nathalie Rich
- Department of Psychosis Studies, Institute of Psychiatry, Psychology & Neuroscience, King's College London, London, UK
| | - Aviv Segev
- Department of Psychosis Studies, Institute of Psychiatry, Psychology & Neuroscience, King's College London, London, UK
| | - Kyra-Verena Sendt
- Department of Psychosis Studies, Institute of Psychiatry, Psychology & Neuroscience, King's College London, London, UK
| | - James H MacCabe
- Department of Psychosis Studies, Institute of Psychiatry, Psychology & Neuroscience, King's College London, London, UK
- NIHR Biomedical Research Centre at South London and Maudsley NHS Foundation Trust, London, UK
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Lencz T, Moyett A, Argyelan M, Barber AD, Cholewa J, Birnbaum ML, Gallego JA, John M, Szeszko PR, Robinson DG, Malhotra AK. Frontal lobe fALFF measured from resting-state fMRI as a prognostic biomarker in first-episode psychosis. Neuropsychopharmacology 2022; 47:2245-2251. [PMID: 36198875 PMCID: PMC9630308 DOI: 10.1038/s41386-022-01470-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/13/2022] [Revised: 09/05/2022] [Accepted: 09/21/2022] [Indexed: 11/09/2022]
Abstract
Clinical response to antipsychotic drug treatment is highly variable, yet prognostic biomarkers are lacking. The goal of the present study was to test whether the fractional amplitude of low-frequency fluctuations (fALFF), as measured from baseline resting-state fMRI data, can serve as a potential biomarker of treatment response to antipsychotics. Patients in the first episode of psychosis (n = 126) were enrolled in two prospective studies employing second-generation antipsychotics (risperidone or aripiprazole). Patients were scanned at the initiation of treatment on a 3T MRI scanner (Study 1, GE Signa HDx, n = 74; Study 2, Siemens Prisma, n = 52). Voxelwise fALFF derived from baseline resting-state fMRI scans served as the primary measure of interest, providing a hypothesis-free (as opposed to region-of-interest) search for regions of the brain that might be predictive of response. At baseline, patients who would later meet strict criteria for clinical response (defined as two consecutive ratings of much or very much improved on the CGI, as well as a rating of ≤3 on psychosis-related items of the BPRS-A) demonstrated significantly greater baseline fALFF in bilateral orbitofrontal cortex compared to non-responders. Thus, spontaneous activity in orbitofrontal cortex may serve as a prognostic biomarker of antipsychotic treatment.
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Affiliation(s)
- Todd Lencz
- Department of Psychiatry, Zucker School of Medicine at Hofstra/Northwell, Hempstead, NY, 11550, USA.
- Department of Psychiatry, Division of Research, The Zucker Hillside Hospital Division of Northwell Health, Glen Oaks, NY, 11004, USA.
- Institute for Behavioral Science, The Feinstein Institutes for Medical Research, Manhasset, NY, 11030, USA.
| | - Ashley Moyett
- Department of Psychiatry, Division of Research, The Zucker Hillside Hospital Division of Northwell Health, Glen Oaks, NY, 11004, USA
| | - Miklos Argyelan
- Department of Psychiatry, Zucker School of Medicine at Hofstra/Northwell, Hempstead, NY, 11550, USA
- Department of Psychiatry, Division of Research, The Zucker Hillside Hospital Division of Northwell Health, Glen Oaks, NY, 11004, USA
- Institute for Behavioral Science, The Feinstein Institutes for Medical Research, Manhasset, NY, 11030, USA
| | - Anita D Barber
- Department of Psychiatry, Division of Research, The Zucker Hillside Hospital Division of Northwell Health, Glen Oaks, NY, 11004, USA
- Institute for Behavioral Science, The Feinstein Institutes for Medical Research, Manhasset, NY, 11030, USA
| | - John Cholewa
- Department of Psychiatry, Division of Research, The Zucker Hillside Hospital Division of Northwell Health, Glen Oaks, NY, 11004, USA
| | - Michael L Birnbaum
- Department of Psychiatry, Zucker School of Medicine at Hofstra/Northwell, Hempstead, NY, 11550, USA
- Department of Psychiatry, Division of Research, The Zucker Hillside Hospital Division of Northwell Health, Glen Oaks, NY, 11004, USA
- Institute for Behavioral Science, The Feinstein Institutes for Medical Research, Manhasset, NY, 11030, USA
| | - Juan A Gallego
- Department of Psychiatry, Zucker School of Medicine at Hofstra/Northwell, Hempstead, NY, 11550, USA
- Department of Psychiatry, Division of Research, The Zucker Hillside Hospital Division of Northwell Health, Glen Oaks, NY, 11004, USA
- Institute for Behavioral Science, The Feinstein Institutes for Medical Research, Manhasset, NY, 11030, USA
| | - Majnu John
- Department of Psychiatry, Division of Research, The Zucker Hillside Hospital Division of Northwell Health, Glen Oaks, NY, 11004, USA
- Institute for Behavioral Science, The Feinstein Institutes for Medical Research, Manhasset, NY, 11030, USA
- Department of Mathematics, Hofstra University, Hempstead, NY, 11549, USA
| | - Philip R Szeszko
- James J. Peters VA Medical Center, Bronx, NY, 10468, USA
- Department of Psychiatry, Icahn School of Medicine at Mount Sinai, New York, NY, 10029, USA
| | - Delbert G Robinson
- Department of Psychiatry, Zucker School of Medicine at Hofstra/Northwell, Hempstead, NY, 11550, USA
- Department of Psychiatry, Division of Research, The Zucker Hillside Hospital Division of Northwell Health, Glen Oaks, NY, 11004, USA
- Institute for Behavioral Science, The Feinstein Institutes for Medical Research, Manhasset, NY, 11030, USA
| | - Anil K Malhotra
- Department of Psychiatry, Zucker School of Medicine at Hofstra/Northwell, Hempstead, NY, 11550, USA
- Department of Psychiatry, Division of Research, The Zucker Hillside Hospital Division of Northwell Health, Glen Oaks, NY, 11004, USA
- Institute for Behavioral Science, The Feinstein Institutes for Medical Research, Manhasset, NY, 11030, USA
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Ferrara M, Franchini G, Funaro M, Cutroni M, Valier B, Toffanin T, Palagini L, Zerbinati L, Folesani F, Murri MB, Caruso R, Grassi L. Machine Learning and Non-Affective Psychosis: Identification, Differential Diagnosis, and Treatment. Curr Psychiatry Rep 2022; 24:925-936. [PMID: 36399236 PMCID: PMC9780131 DOI: 10.1007/s11920-022-01399-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Accepted: 10/12/2022] [Indexed: 11/19/2022]
Abstract
PURPOSE OF REVIEW This review will cover the most relevant findings on the use of machine learning (ML) techniques in the field of non-affective psychosis, by summarizing the studies published in the last three years focusing on illness detection and treatment. RECENT FINDINGS Multiple ML tools that include mostly supervised approaches such as support vector machine, gradient boosting, and random forest showed promising results by applying these algorithms to various sources of data: socio-demographic information, EEG, language, digital content, blood biomarkers, neuroimaging, and electronic health records. However, the overall performance, in the binary classification case, varied from 0.49, which is to be considered very low (i.e., noise), to over 0.90. These results are fully justified by different factors, some of which may be attributable to the preprocessing of the data, the wide variety of the data, and the a-priori setting of hyperparameters. One of the main limitations of the field is the lack of stratification of results based on biological sex, given that psychosis presents differently in men and women; hence, the necessity to tailor identification tools and data analytic strategies. Timely identification and appropriate treatment are key factors in reducing the consequences of psychotic disorders. In recent years, the emergence of new analytical tools based on artificial intelligence such as supervised ML approaches showed promises as a potential breakthrough in this field. However, ML applications in everyday practice are still in its infancy.
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Affiliation(s)
- Maria Ferrara
- Department of Neuroscience and Rehabilitation, Institute of Psychiatry, University of Ferrara, via Fossato di Mortara 64/A, Ferrara, Italy.
- Department of Psychiatry, Yale School of Medicine, 34 Park Street, New Haven, CT, USA.
| | - Giorgia Franchini
- Department of Physics, Informatics and Mathematics, University of Modena and Reggio Emilia, Via Campi 213/B, Modena, Italy
- Department of Mathematics and Computer Science, University of Ferrara, Via Macchiavelli 33, Ferrara, Italy
| | - Melissa Funaro
- Harvey Cushing/John Hay Whitney Medical Library, Yale University, 333 Cedar St., New Haven, CT, USA
| | - Marcello Cutroni
- Department of Neuroscience and Rehabilitation, Institute of Psychiatry, University of Ferrara, via Fossato di Mortara 64/A, Ferrara, Italy
| | - Beatrice Valier
- Department of Neuroscience and Rehabilitation, Institute of Psychiatry, University of Ferrara, via Fossato di Mortara 64/A, Ferrara, Italy
| | - Tommaso Toffanin
- Department of Neuroscience and Rehabilitation, Institute of Psychiatry, University of Ferrara, via Fossato di Mortara 64/A, Ferrara, Italy
| | - Laura Palagini
- Department of Neuroscience and Rehabilitation, Institute of Psychiatry, University of Ferrara, via Fossato di Mortara 64/A, Ferrara, Italy
| | - Luigi Zerbinati
- Department of Neuroscience and Rehabilitation, Institute of Psychiatry, University of Ferrara, via Fossato di Mortara 64/A, Ferrara, Italy
| | - Federica Folesani
- Department of Neuroscience and Rehabilitation, Institute of Psychiatry, University of Ferrara, via Fossato di Mortara 64/A, Ferrara, Italy
| | - Martino Belvederi Murri
- Department of Neuroscience and Rehabilitation, Institute of Psychiatry, University of Ferrara, via Fossato di Mortara 64/A, Ferrara, Italy
| | - Rosangela Caruso
- Department of Neuroscience and Rehabilitation, Institute of Psychiatry, University of Ferrara, via Fossato di Mortara 64/A, Ferrara, Italy
| | - Luigi Grassi
- Department of Neuroscience and Rehabilitation, Institute of Psychiatry, University of Ferrara, via Fossato di Mortara 64/A, Ferrara, Italy
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Takamiya A, Kishimoto T. Is this the end of precision medicine? Or the beginning? Lancet Psychiatry 2022; 9:849-850. [PMID: 36228646 DOI: 10.1016/s2215-0366(22)00336-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/25/2022] [Accepted: 09/07/2022] [Indexed: 11/24/2022]
Affiliation(s)
- Akihiro Takamiya
- Neuropsychiatry Department, Keio University School of Medicine, Tokyo, Japan
| | - Taishiro Kishimoto
- Neuropsychiatry Department, Keio University School of Medicine, Tokyo, Japan; Hills Joint Research Laboratory for Future Preventive Medicine and Wellness, Keio University School of Medicine, Tokyo, Japan.
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Nelson EA, Kraguljac NV, Maximo JO, Armstrong W, Lahti AC. Dorsal striatial hypoconnectivity predicts antipsychotic medication treatment response in first-episode psychosis and unmedicated patients with schizophrenia. Brain Behav 2022; 12:e2625. [PMID: 36237115 PMCID: PMC9660417 DOI: 10.1002/brb3.2625] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/04/2021] [Revised: 03/28/2022] [Accepted: 04/24/2022] [Indexed: 01/10/2023] Open
Abstract
INTRODUCTION The dorsal striatum, comprised of the caudate and putamen, is implicated in the pathophysiology of psychosis spectrum disorders. Given the high concentration of dopamine receptors in the striatum, striatal dopamine imbalance is a likely cause in cortico-striatal dysconnectivity. There is great interest in understanding the relationship between striatal abnormalities in psychosis and antipsychotic treatment response, but few studies have considered differential involvement of the caudate and putamen. This study's goals were twofold. First, identify patterns of dorsal striatal dysconnectivity for the caudate and putamen separately in patients with a psychosis spectrum disorder; second, determine if these dysconnectivity patterns were predictive of treatment response. METHODS Using resting state functional connectivity, we evaluated dorsal striatal connectivity using separate bilateral caudate and putamen seed regions in two cohorts of subjects: a cohort of 71 medication-naïve first episode psychosis patients and a cohort of 42 unmedicated patients with schizophrenia (along with matched controls). Patient and control connectivity maps were contrasted for each cohort. After receiving 6 weeks of risperidone treatment, patients' clinical response was calculated. We used regression analyses to determine the relationship between baseline dysconnectivity and treatment response. RESULTS This dysconnectivity was also predictive of treatment response in both cohorts. DISCUSSION These findings suggest that the caudate may be more of a driving factor than the putamen in early cortico-striatal dysconnectivity.
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Affiliation(s)
- Eric A Nelson
- Department of Psychiatry and Behavioral Neurobiology, University of Alabama at Birmingham, Birmingham, Alabama, USA
| | - Nina V Kraguljac
- Department of Psychiatry and Behavioral Neurobiology, University of Alabama at Birmingham, Birmingham, Alabama, USA
| | - Jose O Maximo
- Department of Psychiatry and Behavioral Neurobiology, University of Alabama at Birmingham, Birmingham, Alabama, USA
| | - William Armstrong
- Department of Psychiatry and Behavioral Neurobiology, University of Alabama at Birmingham, Birmingham, Alabama, USA
| | - Adrienne C Lahti
- Department of Psychiatry and Behavioral Neurobiology, University of Alabama at Birmingham, Birmingham, Alabama, USA
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Nestor PG, Levin LK, Stone WS, Giuliano AJ, Seidman LJ, Levitt JJ. Brain structural abnormalities of the associative striatum in adolescents and young adults at genetic high-risk of schizophrenia: Implications for illness endophenotypes. J Psychiatr Res 2022; 155:355-362. [PMID: 36179416 DOI: 10.1016/j.jpsychires.2022.08.027] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/23/2022] [Revised: 08/26/2022] [Accepted: 08/31/2022] [Indexed: 11/15/2022]
Abstract
OBJECTIVE Dysfunction in cortico-striatal circuitry represents a core component of the pathophysiology in schizophrenia (SZ) but its potential as a candidate endophenotype of the illness is often confounded by neuroleptic medication. METHODS Accordingly, 26 adolescent and young adult participants at genetic high-risk for schizophrenia, but who were asymptomatic and neuroleptic naïve, and 28 age-matched controls underwent 1.5T structural magnetic resonance imaging of the striatum, manually parcellated into limbic (LST), associative (AST), and sensorimotor (SMST) functional subregions. RESULTS In relation to their age peers, participants at genetic high-risk for schizophrenia showed overall lower striatal gray matter volume with their most pronounced loss, bilaterally in the AST, but not the LST or SMST. Neuropsychological testing revealed reduced executive functioning for genetically at-risk participants, although the groups did not differ significantly in overall intelligence or oral reading. For controls but not for at-genetic high-risk participants, stronger executive functioning correlated with increased bilateral AST volume. CONCLUSIONS Reduced bilateral AST volume in genetic high-risk adolescents and young adults, accompanied by heritable loss of higher cognitive brain-behavior relationships, might serve as a useful endophenotype of SZ.
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Affiliation(s)
- Paul G Nestor
- Department of Psychology, University of Massachusetts, Boston, MA, USA; Clinical Neuroscience Division, Laboratory of Neuroscience, Department of Psychiatry, VA Boston Healthcare System, Brockton Division, Brockton, MA, 02301, USA; Harvard Medical School, Boston, MA, 02115, USA
| | - Laura K Levin
- Psychiatry Neuroimaging Laboratory, Department of Psychiatry, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, 02215, USA
| | - William S Stone
- Department of Psychiatry, Beth Israel Deaconess Medical Center, Massachusetts Mental Health Center, Harvard Medical School, Boston, MA, 02115, USA
| | - Anthony J Giuliano
- Department of Psychiatry, Beth Israel Deaconess Medical Center, Massachusetts Mental Health Center, Harvard Medical School, Boston, MA, 02115, USA
| | - Larry J Seidman
- Department of Psychiatry, Beth Israel Deaconess Medical Center, Massachusetts Mental Health Center, Harvard Medical School, Boston, MA, 02115, USA
| | - James J Levitt
- Clinical Neuroscience Division, Laboratory of Neuroscience, Department of Psychiatry, VA Boston Healthcare System, Brockton Division, Brockton, MA, 02301, USA; Harvard Medical School, Boston, MA, 02115, USA; Psychiatry Neuroimaging Laboratory, Department of Psychiatry, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, 02215, USA.
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Rubio JM, Perez-Rodriguez M. Chronic Use of Antipsychotics in Schizophrenia: Are We Asking the Right Question? SCHIZOPHRENIA BULLETIN OPEN 2022; 3:sgac059. [PMID: 36277256 PMCID: PMC9577501 DOI: 10.1093/schizbullopen/sgac059] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Subscribe] [Scholar Register] [Indexed: 11/06/2022]
Abstract
There is an ongoing debate about the potential risks and benefits of long-term antipsychotic treatment in schizophrenia. The data for and against the chronic use of these medicines is mostly indirect, either from observational studies potentially exposed to reverse causation bias or randomized controlled studies that do not cover beyond 2–3 years. We propose that perseverating on the question of what positive or negative outcomes are causally associated with chronic antipsychotic treatment may not lead to better answers than the limited ones that we have, given the limited feasibility of more conclusive studies. Rather, we argue that addressing the research question of the risks and benefits of antipsychotic discontinuation from a perspective of personalized medicine, can be more productive and meaningful to people living with schizophrenia. To this end, research that can quantify the risk of relapse after treatment continuation for a given individual should be prioritized. We make the case that clinically feasible neuroimaging biomarkers have demonstrated promise in related paradigms, and that could be offering a way past this long debate on the risks and benefits of chronic antipsychotic use.
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Affiliation(s)
- Jose M Rubio
- To whom correspondence should be addressed; Donald and Barbara Zucker School of Medicine at Hofstra/Nortwhell, Hempstead, NY, USA; tel: 7184705912, e-mail:
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Graph-Theory-Based Degree Centrality Combined with Machine Learning Algorithms Can Predict Response to Treatment with Antipsychotic Medications in Patients with First-Episode Schizophrenia. DISEASE MARKERS 2022; 2022:1853002. [PMID: 36277973 PMCID: PMC9584695 DOI: 10.1155/2022/1853002] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 03/07/2022] [Revised: 07/26/2022] [Accepted: 08/16/2022] [Indexed: 11/22/2022]
Abstract
Objectives Schizophrenia (SCZ) is associated with disrupted functional brain connectivity, and antipsychotic medications are the primary and most commonly used treatment for schizophrenia. However, not all patients respond to antipsychotic medications. Methods The study is aimed at investigating whether the graph-theory-based degree centrality (DC), derived from resting-state functional MRI (rs-fMRI), can predict the treatment outcomes. rs-fMRI data from 38 SCZ patients were collected and compared with findings from 38 age- and gender-matched healthy controls (HCs). The patients were treated with antipsychotic medications for 16 weeks before undergoing a second rs-fMRI scan. DC data were processed using DPABI and SPM12 software. Results SCZ patients at baseline showed increased DC in the frontal and temporal gyrus, anterior cingulate cortex, and precuneus and reduced DC in bilateral subcortical gray matter structures. However, those abnormalities showed a clear renormalization after antipsychotic medication treatments. Support vector machine analysis using leave-one-out cross-validation achieved a correct classification rate of 84.2% (sensitivity 78.9%, specificity 89.5%, and area under the receiver operating characteristic curve (AUC) 0.925) for differentiating effective subjects from ineffective subjects. Brain areas that contributed most to the classification model were mainly located within the bilateral putamen, left inferior frontal gyrus, left middle occipital cortex, bilateral middle frontal gyrus, left cerebellum, left medial frontal gyrus, left inferior temporal gyrus, and left angular. Furthermore, the DC change within the bilateral putamen is negatively correlated with the symptom improvements after treatment. Conclusions Our study confirmed that graph-theory-based measures, combined with machine-learning algorithms, can provide crucial insights into pathophysiological mechanisms and the effectiveness of antipsychotic medications.
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Sonnenschein SF, Mayeli A, Yushmanov VE, Blazer A, Calabro FJ, Perica M, Foran W, Luna B, Hetherington HP, Ferrarelli F, Sarpal DK. A longitudinal investigation of GABA, glutamate, and glutamine across the insula during antipsychotic treatment of first-episode schizophrenia. Schizophr Res 2022; 248:98-106. [PMID: 36029656 PMCID: PMC10018530 DOI: 10.1016/j.schres.2022.08.008] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/15/2022] [Revised: 06/29/2022] [Accepted: 08/20/2022] [Indexed: 11/23/2022]
Abstract
Individuals with first-episode schizophrenia (FES) typically present with acute psychotic symptoms. Though antipsychotic drugs are the mainstay for treatment, the neurobiology underlying successful treatment remains largely elusive. Recent evidence from functional connectivity studies highlights the insula as a key structure in the neural mechanism of response. However, molecular contributions to response across insular regions remain largely unknown. We used 7-Tesla magnetic resonance spectroscopic imaging (MRSI) to measure glutamate (Glu), Glutamine (Gln), and GABA from anterior and posterior regions of the insula across antipsychotic treatment. A total of 36 participants were examined, including 15 individuals with FES and moderate to severe psychosis who were scanned at two time points, while starting and after 6 weeks of antipsychotic treatment. Symptoms were carefully monitored across the study period to characterize treatment response. GABA, Glu, and Gln levels were calculated relative to creatine in anterior and posterior insular regions, bilaterally. In relation to psychotic symptom reduction, we observed a significant increase in Glu across all insular regions with (p < 0.001), but no corresponding changes in Gln or GABA. In group analyses, the FES cohort showed lower levels of Glu (p < 0.001) and GABA (p = 0.02) at baseline. Finally, in exploratory analyses, treatment remitters demonstrated a normalization of lower insular Glu levels across treatment, unlike non-remitters. Overall, these findings contribute to our understating of molecular changes associated with antipsychotic response and demonstrate abnormalities specific to the insula in FES.
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Affiliation(s)
| | - Ahmad Mayeli
- Department of Psychiatry, University of Pittsburgh, Pittsburgh, PA, USA
| | | | - Annie Blazer
- Department of Psychiatry, University of Pittsburgh, Pittsburgh, PA, USA
| | - Finnegan J Calabro
- Department of Psychiatry, University of Pittsburgh, Pittsburgh, PA, USA; Department of Bioengineering, University of Pittsburgh, Pittsburgh, PA, USA
| | - Maria Perica
- Department of Psychiatry, University of Pittsburgh, Pittsburgh, PA, USA
| | - William Foran
- Department of Psychiatry, University of Pittsburgh, Pittsburgh, PA, USA
| | - Beatriz Luna
- Department of Psychiatry, University of Pittsburgh, Pittsburgh, PA, USA; Department of Psychology, University of Pittsburgh, Pittsburgh, PA, USA; Department of Pediatrics, University of Pittsburgh, Pittsburgh, PA, USA
| | | | - Fabio Ferrarelli
- Department of Psychiatry, University of Pittsburgh, Pittsburgh, PA, USA
| | - Deepak K Sarpal
- Department of Psychiatry, University of Pittsburgh, Pittsburgh, PA, USA.
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Blazer A, Chengappa KNR, Foran W, Parr AC, Kahn CE, Luna B, Sarpal DK. Changes in corticostriatal connectivity and striatal tissue iron associated with efficacy of clozapine for treatment‑resistant schizophrenia. Psychopharmacology (Berl) 2022; 239:2503-2514. [PMID: 35435461 PMCID: PMC9013738 DOI: 10.1007/s00213-022-06138-0] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/23/2021] [Accepted: 03/31/2022] [Indexed: 12/02/2022]
Abstract
RATIONALE Though numerous studies demonstrate the superiority of clozapine (CLZ) for treatment of persistent psychotic symptoms that are characteristic of treatment-refractory schizophrenia (TRS), what remains unknown are the neural and molecular mechanisms underlying CLZ's efficacy. Recent work implicates increased corticostriatal functional connectivity as a marker of response to non-CLZ, dopamine (DA) D2-receptor blocking antipsychotic drugs. However, it is undetermined whether this connectivity finding also relates to CLZ's unique efficacy, or if response to CLZ is associated with changes in striatal DA functioning. OBJECTIVE In a cohort of 22 individuals with TRS, we examined response to CLZ in relation to the following: (1) change in corticostriatal functional connectivity; and (2) change in a magnetic resonance-based measure of striatal tissue iron (R2'), which demonstrates utility as a proxy measure for elements of DA functioning. METHODS Participants underwent scanning while starting CLZ and after 12 weeks of CLZ treatment. We used both cortical and striatal regions of interest to examine changes in corticostriatal interactions and striatal R2' in relation to CLZ response (% reduction of psychotic symptoms). RESULTS We first found that response to CLZ was associated with an increase in corticostriatal connectivity between the dorsal caudate and regions of the frontoparietal network (P < 0.05, corrected). Secondly, we observed no significant changes in striatal R2' across CLZ treatment. CONCLUSION Overall, these results indicate that changes in corticostriatal networks without gross shifts in striatal DA functioning underlies CLZ response. Our results provide novel mechanistic insight into response to CLZ treatment.
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Affiliation(s)
- Annie Blazer
- Department of Psychiatry, University of Pittsburgh, 3811 O'Hara Street, Pittsburgh, PA, 15213, USA
| | - K N Roy Chengappa
- Department of Psychiatry, University of Pittsburgh, 3811 O'Hara Street, Pittsburgh, PA, 15213, USA
| | - William Foran
- Department of Psychiatry, University of Pittsburgh, 3811 O'Hara Street, Pittsburgh, PA, 15213, USA
| | - Ashley C Parr
- Department of Psychiatry, University of Pittsburgh, 3811 O'Hara Street, Pittsburgh, PA, 15213, USA
| | - Charles E Kahn
- Department of Psychiatry, University of Pittsburgh, 3811 O'Hara Street, Pittsburgh, PA, 15213, USA
| | - Beatriz Luna
- Department of Psychiatry, University of Pittsburgh, 3811 O'Hara Street, Pittsburgh, PA, 15213, USA
- Department of Psychology, University of Pittsburgh, Pittsburgh, PA, USA
| | - Deepak K Sarpal
- Department of Psychiatry, University of Pittsburgh, 3811 O'Hara Street, Pittsburgh, PA, 15213, USA.
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Sonnenschein SF, Parr AC, Larsen B, Calabro FJ, Foran W, Eack SM, Luna B, Sarpal DK. Subcortical brain iron deposition in individuals with schizophrenia. J Psychiatr Res 2022; 151:272-278. [PMID: 35523067 DOI: 10.1016/j.jpsychires.2022.04.013] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/06/2021] [Revised: 04/01/2022] [Accepted: 04/18/2022] [Indexed: 11/28/2022]
Abstract
Subcortical structures play a critical role the pathophysiology and treatment of schizophrenia (SZ), yet underlying neurophysiological processes, in vivo, remain largely unexplored. Brain tissue iron, which can be measured with magnetic resonance-based methods, is a crucial component of a variety of neuronal functions including neurotransmitter synthesis. Here we used a proxy measure of tissue iron to examine basal ganglia and thalamic structures in an adult cohort of individuals with chronic SZ. A publicly available dataset of 72 individuals with SZ between ages 18 and 65, and a matched sample of 74 healthy control (HC) participants were included. A novel method that calculated the inverse-normalized T2*-weighted contrast (1/nT2*) was used to estimate brain iron within the basal ganglia and thalamus. Between group, age- and sex-related differences in 1/nT2* were examined, in addition to correlations with measures of psychopathology and cognition. Individuals with SZ showed greater 1/nT2* (iron index) compared to HCs in the thalamus (p < 0.01, FWE corrected). Age-related 1/nT2* accumulation was noted in regions of the basal ganglia, coinciding with prior work, and prominent sex-differences were noted in the caudate and thalamus (p < 0.01, FWE corrected). No significant relationship was observed between 1/nT2* and measures of neurocognition or psychopathology. Overall, our findings characterize a non-invasive proxy measure of tissue iron in SZ and highlight thalamic iron accumulation as a potential marker of illness.
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Affiliation(s)
| | | | - Bart Larsen
- Department of Psychiatry, University of Pennsylvania, Philadelphia, PA, USA
| | | | | | - Shaun M Eack
- Department of Psychiatry, USA; School of Social Work, USA
| | - Beatriz Luna
- Department of Psychiatry, USA; Department of Psychology, USA; Department of Pediatrics, University of Pittsburgh, Pittsburgh, PA, USA
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Chaudhary S, Roy A, Summers C, Zhornitsky S, Ahles T, Li CSR, Chao HH. Hypothalamic connectivities predict individual differences in ADT-elicited changes in working memory and quality of life in prostate cancer patients. Sci Rep 2022; 12:9567. [PMID: 35688928 PMCID: PMC9187668 DOI: 10.1038/s41598-022-13361-4] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/18/2022] [Accepted: 05/24/2022] [Indexed: 11/09/2022] Open
Abstract
Androgen deprivation therapy (ADT) has been associated with adverse effects on cognition. However, we currently lack understanding of the neurobiology and prognostic markers of these effects. Given that ADT acts via the hypothalamus-pituitary-gonadal axis, we assessed whether baseline hypothalamic resting state functional connectivity (rsFC) could predict changes in working memory and quality of life in prostate cancer patients following androgen deprivation. In a prospective observational study, 28 men with non-metastatic prostate cancer receiving ADT and 38 patients not receiving ADT (controls), matched in age, years of education and Montreal Cognitive Assessment score, participated in brain imaging at baseline, and N-back task and quality-of-life (QoL) assessments at baseline and at 6 months follow-up. Imaging data were processed with published routines and evaluated at a corrected threshold. ADT and control groups did not differ in N-back performance or QoL across time points. In ADT, the changes in 0-back correct response rate (follow-up-baseline) were correlated with baseline hypothalamus-precentral gyrus rsFC; the changes in 1-back correct response rate and reaction time were each correlated with hypothalamus-middle frontal gyrus and superior parietal lobule rsFC. The changes in physical well-being subscore of QoL were correlated with baseline hypothalamus-anterior cingulate and cuneus rsFC. The hypothalamus rsFCs predicted N-back and QoL change with an area under the receiver operating characteristic curve of 0.93 and 0.73, respectively. Baseline hypothalamus-frontoparietal and salience network rsFC's predict inter-subject variations in the changes in working-memory and QoL following 6 months of ADT. Whether and how hypothalamic rsFCs may predict the cognitive and QoL effects with longer-term ADT remain to be investigated.
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Affiliation(s)
- Shefali Chaudhary
- Department of Psychiatry, Yale University School of Medicine, CMHC S110, 34 Park Street, New Haven, CT, 06519, USA.
| | - Alicia Roy
- Cancer Center, VA Connecticut Healthcare System, 950 Campbell Avenue, West Haven, CT, 06516, USA
| | - Christine Summers
- Cancer Center, VA Connecticut Healthcare System, 950 Campbell Avenue, West Haven, CT, 06516, USA
| | - Simon Zhornitsky
- Department of Psychiatry, Yale University School of Medicine, CMHC S110, 34 Park Street, New Haven, CT, 06519, USA
| | - Tim Ahles
- Department of Psychiatry and Behavioral Sciences, Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | - Chiang-Shan R Li
- Department of Psychiatry, Yale University, New Haven, CT, 06520, USA
- Department of Neuroscience, Yale University, New Haven, CT, 06520, USA
- Interdepartmental Neuroscience Program, Yale University School of Medicine, Yale University, New Haven, CT, 06520, USA
- Wu Tsai Institute, Yale University, New Haven, CT, 06520, USA
| | - Herta H Chao
- Cancer Center, VA Connecticut Healthcare System, 950 Campbell Avenue, West Haven, CT, 06516, USA.
- Department of Medicine and Yale Comprehensive Cancer Center, Yale University School of Medicine, New Haven, CT, 06519, USA.
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Rubio JM, Kane JM. The pharmacological treatment of schizophrenia: How far have we come? PCN REPORTS : PSYCHIATRY AND CLINICAL NEUROSCIENCES 2022; 1:e13. [PMID: 38868633 PMCID: PMC11114354 DOI: 10.1002/pcn5.13] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/09/2022] [Revised: 04/11/2022] [Accepted: 04/13/2022] [Indexed: 06/14/2024]
Abstract
Schizophrenia is a chronic and often severe mental disorder for which antipsychotic drugs are the cornerstone of treatment. Although the essential mechanism of action of these drugs has not changed much since they were first discovered in the 1950s, there have been numerous advances in the context in which these drugs are prescribed, as well as in the considerations for their optimal use. In this review, we summarize five selected issues in which the psychopharmacological treatment of schizophrenia has most evolved. Namely, these are the shift of outcomes of interest from symptoms to recovery, the development of stratified approaches to select the most appropriate treatment for each individual, the recognition of treatment nonadherence as a critical factor determining outcomes, the recommendations for maintenance treatment, and, finally, the promise of new antipsychotic compounds that innovate in their mechanisms of action, improving efficacy/safety profiles. Finally, we discuss how some of these advances have already delivered to improved outcomes in the real world, whereas others have demonstrated efficacy under optimal circumstances yet have not been translated into better outcomes in the community. Thus, the road ahead includes both identifying novel treatments that engage the psychopathology of the illness and improve the efficacy/tolerability profile of currently available agents, as well as developing interventions that mitigate the barriers for the use of novel interventions, some of them already existing, in the real world.
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Affiliation(s)
- Jose M. Rubio
- Donald and Barbara Zucker School of Medicine at Hofstra—Northwell, Feinstein Institutes of Medical Research—Institute of Behavioral ScienceZucker Hillside Hospital—Northwell HealthGlen OaksNYUnited States
| | - John M. Kane
- Donald and Barbara Zucker School of Medicine at Hofstra—Northwell, Feinstein Institutes of Medical Research—Institute of Behavioral ScienceZucker Hillside Hospital—Northwell HealthGlen OaksNYUnited States
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Zong X, He C, Huang X, Xiao J, Li L, Li M, Yao T, Hu M, Liu Z, Duan X, Zheng J. Predictive Biomarkers for Antipsychotic Treatment Response in Early Phase of Schizophrenia: Multi-Omic Measures Linking Subcortical Covariant Network, Transcriptomic Signatures, and Peripheral Epigenetics. Front Neurosci 2022; 16:853186. [PMID: 35615285 PMCID: PMC9125083 DOI: 10.3389/fnins.2022.853186] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/12/2022] [Accepted: 04/07/2022] [Indexed: 11/13/2022] Open
Abstract
Background Volumetric alterations of subcortical structures as predictors of antipsychotic treatment response have been previously corroborated, but less is known about whether their morphological covariance relates to treatment outcome and is driven by gene expression and epigenetic modifications. Methods Subcortical volumetric covariance was analyzed by using baseline T1-weighted magnetic resonance imaging (MRI) in 38 healthy controls and 38 drug-naïve first-episode schizophrenia patients. Patients were treated with 8-week risperidone monotherapy and divided into responder and non-responder groups according to the Remission in Schizophrenia Working Group (RSWG). We utilized partial least squares (PLS) regression to examine the spatial associations between gene expression of subcortical structures from a publicly available transcriptomic dataset and between-group variances of structural covariance. The peripheral DNA methylation (DNAm) status of a gene of interest (GOI), overlapping between genes detected in the PLS and 108 schizophrenia candidate gene loci previously reported, was examined in parallel with MRI scanning. Results In the psychotic symptom dimension, non-responders had a higher baseline structural covariance in the putamen-hippocampus-pallidum-accumbens pathway compared with responders. For disorganized symptoms, significant differences in baseline structural covariant connections were found in the putamen-hippocampus-pallidum-thalamus circuit between the two subgroups. The imaging variances related to psychotic symptom response were spatially related to the expression of genes enriched in neurobiological processes and dopaminergic pathways. The DNAm of GOI demonstrated significant associations with patients' improvement of psychotic symptoms. Conclusion Baseline subcortical structural covariance and peripheral DNAm may relate to antipsychotic treatment response. Phenotypic variations in subcortical connectome related to psychotic symptom response may be transcriptomically and epigenetically underlaid. This study defines a roadmap for future studies investigating multimodal imaging epigenetic biomarkers for treatment response in schizophrenia.
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Affiliation(s)
- Xiaofen Zong
- Department of Psychiatry, Renmin Hospital of Wuhan University, Wuhan, China
| | - Changchun He
- The High-Field Magnetic Resonance Brain Imaging Key Laboratory of Sichuan Province, University of Electronic Science and Technology of China, Chengdu, China
| | - Xinyue Huang
- The High-Field Magnetic Resonance Brain Imaging Key Laboratory of Sichuan Province, University of Electronic Science and Technology of China, Chengdu, China
| | - Jinming Xiao
- The High-Field Magnetic Resonance Brain Imaging Key Laboratory of Sichuan Province, University of Electronic Science and Technology of China, Chengdu, China
| | - Lei Li
- The High-Field Magnetic Resonance Brain Imaging Key Laboratory of Sichuan Province, University of Electronic Science and Technology of China, Chengdu, China
| | - Meiling Li
- Department of Radiology, The Athinoula A. Martinos Center for Biomedical Imaging, Harvard Medical School, Massachusetts General Hospital, Boston, MA, United States
| | - Tao Yao
- Department of Psychiatry, Renmin Hospital of Wuhan University, Wuhan, China
| | - Maolin Hu
- Department of Psychiatry, Renmin Hospital of Wuhan University, Wuhan, China
| | - Zhongchun Liu
- Department of Psychiatry, Renmin Hospital of Wuhan University, Wuhan, China
| | - Xujun Duan
- The High-Field Magnetic Resonance Brain Imaging Key Laboratory of Sichuan Province, University of Electronic Science and Technology of China, Chengdu, China
| | - Junjie Zheng
- The Early Intervention Unit, Department of Psychiatry, Affiliated Nanjing Brain Hospital, Nanjing Medical University, Nanjing, China
- The Functional Brain Imaging Institute, Nanjing Medical University, Nanjing, China
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Striatal functional connectivity in psychosis relapse: A hypothesis generating study. Schizophr Res 2022; 243:342-348. [PMID: 34183210 DOI: 10.1016/j.schres.2021.06.010] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/16/2021] [Revised: 05/12/2021] [Accepted: 06/18/2021] [Indexed: 12/31/2022]
Abstract
Most individuals with psychotic disorders relapse over their course of illness, yet the neural processes that may lead to symptom worsening are poorly understood. Importantly, such processes could be potentially affected by antipsychotic adherence status upon relapse (i.e., relapse despite ongoing antipsychotic maintenance vs following antipsychotic discontinuation), reflecting distinct mechanisms. As a first foray into this question, we aim to compare the striatal connectivity index (SCI), a biomarker derived from striatal resting state functional connectivity predictive of treatment response, by adherence status upon relapse. In order to confirm adherence status upon relapse, we compared individuals treated with long-acting injectable antipsychotics upon relapse (i.e., breakthrough psychosis) (n = 23), with individuals who had decided to interrupt antipsychotic treatment and then relapsed (n = 27), as well as healthy controls (n = 26). We acquired for each individual >10 min of resting state fMRI, to generate functional connectivity maps. Region of interest (ROI) analyses were conducted to calculate SCI values for each participant. These values were entered as dependent variable in a linear regression adjusted for sex and age for which adherence status was the independent variable. Individuals in the breakthrough psychosis group had significantly lower SCI values than healthy controls (Cohen's d = 0.99, p < 0.001), and non-adherent individuals upon relapse (Cohen's d = 0.58, p = 0.032), whereas non-adherent individuals had also trend level lower SCI values than healthy controls (Cohen's d = 0.44, p = 0.09). These results suggest the hypothesis that striatal functional connectivity may be aberrant in psychosis relapse, and that this dysfunction may be greater among individuals who developed relapse despite ongoing antipsychotic treatment.
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Zhao M, Yan W, Luo N, Zhi D, Fu Z, Du Y, Yu S, Jiang T, Calhoun VD, Sui J. An attention-based hybrid deep learning framework integrating brain connectivity and activity of resting-state functional MRI data. Med Image Anal 2022; 78:102413. [PMID: 35305447 PMCID: PMC9035078 DOI: 10.1016/j.media.2022.102413] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/10/2021] [Revised: 10/27/2021] [Accepted: 03/01/2022] [Indexed: 12/30/2022]
Abstract
Functional magnetic resonance imaging (fMRI) as a promising tool to investigate psychotic disorders can be decomposed into useful imaging features such as time courses (TCs) of independent components (ICs) and functional network connectivity (FNC) calculated by TC cross-correlation. TCs reflect the temporal dynamics of brain activity and the FNC characterizes temporal coherence across intrinsic brain networks. Both features have been used as input to deep learning approaches with decent results. However, few studies have tried to leverage their complementary information to learn optimal representations at multiple facets. Motivated by this, we proposed a Hybrid Deep Learning Framework integrating brain Connectivity and Activity (HDLFCA) together by combining convolutional recurrent neural network (C-RNN) and deep neural network (DNN), aiming to improve classification accuracy and interpretability simultaneously. Specifically, C-RNNAM was proposed to extract temporal dynamic dependencies with an attention module (AM) to automatically learn discriminative knowledge from TC nodes, while DNN was applied to identify the most group-discriminative FNC patterns with layer-wise relevance propagation (LRP). Then, both prediction outputs were concatenated to build a new feature matrix, generating the final decision by logistic regression. The effectiveness of HDLFCA was validated on both multi-site schizophrenia (SZ, n ∼ 1100) and public autism datasets (ABIDE, n ∼ 1522) by outperforming 12 alternative models at 2.8-8.9% accuracy, including 8 models using either static FNC or TCs and 4 models using dynamic FNC. Appreciable classification accuracy was achieved for HC vs. SZ (85.3%) and HC vs. Autism (72.4%) respectively. More importantly, the most group-discriminative brain regions can be easily attributed and visualized, providing meaningful biological interpretability and highlighting the great potential of the proposed HDLFCA model in the identification of valid neuroimaging biomarkers.
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Affiliation(s)
- Min Zhao
- Brainnetome Center and National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, Beijing, China; School of Artificial Intelligence, University of Chinese Academy of Sciences, Beijing, China
| | - Weizheng Yan
- Tri-Institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS), Georgia State University, Georgia Institute of Technology, Emory University, Atlanta, GA, USA
| | - Na Luo
- Brainnetome Center and National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, Beijing, China
| | - Dongmei Zhi
- State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing, China
| | - Zening Fu
- Tri-Institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS), Georgia State University, Georgia Institute of Technology, Emory University, Atlanta, GA, USA
| | - Yuhui Du
- School of Computer and Information Technology, Shanxi University, Taiyuan, China
| | - Shan Yu
- Brainnetome Center and National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, Beijing, China; School of Artificial Intelligence, University of Chinese Academy of Sciences, Beijing, China
| | - Tianzi Jiang
- Brainnetome Center and National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, Beijing, China; School of Artificial Intelligence, University of Chinese Academy of Sciences, Beijing, China
| | - Vince D Calhoun
- Tri-Institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS), Georgia State University, Georgia Institute of Technology, Emory University, Atlanta, GA, USA
| | - Jing Sui
- Tri-Institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS), Georgia State University, Georgia Institute of Technology, Emory University, Atlanta, GA, USA; State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing, China.
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Spark DL, Fornito A, Langmead CJ, Stewart GD. Beyond antipsychotics: a twenty-first century update for preclinical development of schizophrenia therapeutics. Transl Psychiatry 2022; 12:147. [PMID: 35393394 PMCID: PMC8991275 DOI: 10.1038/s41398-022-01904-2] [Citation(s) in RCA: 25] [Impact Index Per Article: 12.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/16/2021] [Revised: 03/02/2022] [Accepted: 03/15/2022] [Indexed: 11/15/2022] Open
Abstract
Despite 50+ years of drug discovery, current antipsychotics have limited efficacy against negative and cognitive symptoms of schizophrenia, and are ineffective-with the exception of clozapine-against any symptom domain for patients who are treatment resistant. Novel therapeutics with diverse non-dopamine D2 receptor targets have been explored extensively in clinical trials, yet often fail due to a lack of efficacy despite showing promise in preclinical development. This lack of translation between preclinical and clinical efficacy suggests a systematic failure in current methods that determine efficacy in preclinical rodent models. In this review, we critically evaluate rodent models and behavioural tests used to determine preclinical efficacy, and look to clinical research to provide a roadmap for developing improved translational measures. We highlight the dependence of preclinical models and tests on dopamine-centric theories of dysfunction and how this has contributed towards a self-reinforcing loop away from clinically meaningful predictions of efficacy. We review recent clinical findings of distinct dopamine-mediated dysfunction of corticostriatal circuits in patients with treatment-resistant vs. non-treatment-resistant schizophrenia and suggest criteria for establishing rodent models to reflect such differences, with a focus on objective, translational measures. Finally, we review current schizophrenia drug discovery and propose a framework where preclinical models are validated against objective, clinically informed measures and preclinical tests of efficacy map onto those used clinically.
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Affiliation(s)
- Daisy L Spark
- Drug Discovery Biology, Neuroscience & Mental Health Therapeutic Program Area, and Neuromedicines Discovery Centre, Monash Institute of Pharmaceutical Sciences, Monash University, 381 Royal Parade, Parkville, VIC, 3052, Australia
| | - Alex Fornito
- Turner Institute for Brain and Mental Health, Monash Biomedical Imaging, and School of Psychological Sciences, Monash University, Clayton, VIC, 3800, Australia
| | - Christopher J Langmead
- Drug Discovery Biology, Neuroscience & Mental Health Therapeutic Program Area, and Neuromedicines Discovery Centre, Monash Institute of Pharmaceutical Sciences, Monash University, 381 Royal Parade, Parkville, VIC, 3052, Australia.
| | - Gregory D Stewart
- Drug Discovery Biology, Neuroscience & Mental Health Therapeutic Program Area, and Neuromedicines Discovery Centre, Monash Institute of Pharmaceutical Sciences, Monash University, 381 Royal Parade, Parkville, VIC, 3052, Australia.
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McCutcheon RA, McGuire P. Reinventing schizophrenia: The rules of the game. Schizophr Res 2022; 242:94-95. [PMID: 34998652 DOI: 10.1016/j.schres.2021.12.009] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/18/2021] [Accepted: 12/09/2021] [Indexed: 12/24/2022]
Affiliation(s)
- Robert A McCutcheon
- Department of Psychosis Studies, Institute of Psychiatry, Psychology & Neuroscience, Kings College London, De Crespigny Park, London SE5 8AF, UK.
| | - Philip McGuire
- Department of Psychosis Studies, Institute of Psychiatry, Psychology & Neuroscience, Kings College London, De Crespigny Park, London SE5 8AF, UK
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Matrone M, Kotzalidis GD, Romano A, Bozzao A, Cuomo I, Valente F, Gabaglio C, Lombardozzi G, Trovini G, Amici E, Perrini F, De Persis S, Iasevoli F, De Filippis S, de Bartolomeis A. Treatment-resistant schizophrenia: Addressing white matter integrity, intracortical glutamate levels, clinical and cognitive profiles between early- and adult-onset patients. Prog Neuropsychopharmacol Biol Psychiatry 2022; 114:110493. [PMID: 34883221 DOI: 10.1016/j.pnpbp.2021.110493] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/16/2021] [Revised: 11/05/2021] [Accepted: 11/29/2021] [Indexed: 01/30/2023]
Abstract
BACKGROUND Treatment-resistance in schizophrenia is 30-40%. Its neurobiology remains unclear; to explore it, we conducted a combined spectrometry/tractography/cognitive battery and psychopathological rating study on patients with treatment-resistant schizophrenia (TRS), dividing the sample into early-onset (N = 21) and adult-onset TRS (N = 20). Previous studies did not differentiate between early- (onset 13-18 years) and adult-onset (>18 years at formal diagnosis of schizophrenia) TRS. METHODS We evaluated cross-sectionally 41 TRS patients (26 male and 15 female) and 20 matched healthy controls (HCs) with psychopathological and cognitive testing prior to participating in brain imaging scanning using magnetic resonance spectroscopy and diffusion tensor imaging to determine the relationship between their symptoms and their glutamate levels and white matter integrity. RESULTS TRS patients scored lower than HCs on all cognitive domains; early-onset patients performed better than adult-onset patients only on the Symbol Coding domain. TRS correlated with symptom severity, especially negative symptoms. Glutamate levels and glutamate/creatine were increased in anterior cingulate cortex. Diffusion tensor imaging showed low fractional anisotropy in TRS patients in specific white matter tracts compared to HCs (bilateral anterior thalamic radiation, cortico-spinal tract, forceps minor, inferior fronto-occipital fasciculus, inferior longitudinal fasciculus, superior longitudinal fasciculus, and right uncinate fasciculus). CONCLUSIONS We identified specific magnetic resonance spectroscopy and diffusion tensor imaging alterations in TRS patients. Adult-onset TRS differed little from early-onset TRS on most measures; this points to alterations being present since the outset of schizophrenia and may constitute a biological signature of treatment-resistance.
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Affiliation(s)
- Marta Matrone
- Section of Psychiatry Laboratory of Molecular and Translational Psychiatry, Unit of Treatment Resistant Psychosis, Department of Neuroscience, Reproductive Science, and Odontostomatology, University of Naples Federico II, 80131 Naples, Italy; Clinica Neuropsichiatrica Villa von Siebenthal, Department of Neuropsychiatry, Via della Madonnina 1, 00045 Genzano di Roma, RM, Italy
| | - Georgios D Kotzalidis
- NESMOS (Neurosciences, Mental Health, and Sensory Organs) Department, Sapienza University of Rome, Faculty of Medicine and Psychology, Via di Grottarossa 1035-1039, 00189 Rome, Italy.
| | - Andrea Romano
- NESMOS (Neurosciences, Mental Health, and Sensory Organs) Department, Sapienza University of Rome, Faculty of Medicine and Psychology, Via di Grottarossa 1035-1039, 00189 Rome, Italy.
| | - Alessandro Bozzao
- NESMOS (Neurosciences, Mental Health, and Sensory Organs) Department, Sapienza University of Rome, Faculty of Medicine and Psychology, Via di Grottarossa 1035-1039, 00189 Rome, Italy.
| | - Ilaria Cuomo
- UOC SM I Distretto ASL ROMA 1, C.C. Regina Cœli, Via della Lungara 29, 00165 Rome, Italy.
| | - Francesca Valente
- Clinica Neuropsichiatrica Villa von Siebenthal, Department of Neuropsychiatry, Via della Madonnina 1, 00045 Genzano di Roma, RM, Italy; Department of Human Neurosciences, Institute of Child and Adolescent Neuropsychiatry, Sapienza University of Rome, Italy.
| | - Chiara Gabaglio
- Clinica Neuropsichiatrica Villa von Siebenthal, Department of Neuropsychiatry, Via della Madonnina 1, 00045 Genzano di Roma, RM, Italy
| | - Ginevra Lombardozzi
- Clinica Neuropsichiatrica Villa von Siebenthal, Department of Neuropsychiatry, Via della Madonnina 1, 00045 Genzano di Roma, RM, Italy
| | - Giada Trovini
- Clinica Neuropsichiatrica Villa von Siebenthal, Department of Neuropsychiatry, Via della Madonnina 1, 00045 Genzano di Roma, RM, Italy
| | - Emanuela Amici
- Clinica Neuropsichiatrica Villa von Siebenthal, Department of Neuropsychiatry, Via della Madonnina 1, 00045 Genzano di Roma, RM, Italy
| | - Filippo Perrini
- Clinica Neuropsichiatrica Villa von Siebenthal, Department of Neuropsychiatry, Via della Madonnina 1, 00045 Genzano di Roma, RM, Italy; UOC SMREE Distretto ASL ROMA 6, TSMREE, Via S. Biagio, 12, 00049, Velletri, Rome, Italy.
| | - Simone De Persis
- UOSD Attività Terapeutiche Riabilitative per i Disturbi da uso di Sostanze e nuove Dipendenze, ASL Rieti, Via Salaria per Roma 36, 02100 Rieti, Italy.
| | - Felice Iasevoli
- Section of Psychiatry Laboratory of Molecular and Translational Psychiatry, Unit of Treatment Resistant Psychosis, Department of Neuroscience, Reproductive Science, and Odontostomatology, University of Naples Federico II, 80131 Naples, Italy.
| | - Sergio De Filippis
- Clinica Neuropsichiatrica Villa von Siebenthal, Department of Neuropsychiatry, Via della Madonnina 1, 00045 Genzano di Roma, RM, Italy
| | - Andrea de Bartolomeis
- Section of Psychiatry Laboratory of Molecular and Translational Psychiatry, Unit of Treatment Resistant Psychosis, Department of Neuroscience, Reproductive Science, and Odontostomatology, University of Naples Federico II, 80131 Naples, Italy.
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Wang M, Hu K, Fan L, Yan H, Li P, Jiang T, Liu B. Predicting Treatment Response in Schizophrenia With Magnetic Resonance Imaging and Polygenic Risk Score. Front Genet 2022; 13:848205. [PMID: 35186051 PMCID: PMC8847599 DOI: 10.3389/fgene.2022.848205] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/04/2022] [Accepted: 01/12/2022] [Indexed: 11/15/2022] Open
Abstract
Background: Prior studies have separately demonstrated that magnetic resonance imaging (MRI) and schizophrenia polygenic risk score (PRS) are predictive of antipsychotic medication treatment outcomes in schizophrenia. However, it remains unclear whether MRI combined with PRS can provide superior prognostic performance. Besides, the relative importance of these measures in predictions is not investigated. Methods: We collected 57 patients with schizophrenia, all of which had baseline MRI and genotype data. All these patients received approximately 6 weeks of antipsychotic medication treatment. Psychotic symptom severity was assessed using the Positive and Negative Syndrome Scale (PANSS) at baseline and follow-up. We divided these patients into responders (N = 20) or non-responders (N = 37) based on whether their percentages of PANSS total reduction were above or below 50%. Nine categories of MRI measures and PRSs with 145 different p-value thresholding ranges were calculated. We trained machine learning classifiers with these baseline predictors to identify whether a patient was a responder or non-responder. Results: The extreme gradient boosting (XGBoost) technique was applied to build binary classifiers. Using a leave-one-out cross-validation scheme, we achieved an accuracy of 86% with all MRI and PRS features. Other metrics were also estimated, including sensitivity (85%), specificity (86%), F1-score (81%), and area under the receiver operating characteristic curve (0.86). We found excluding a single feature category of gray matter volume (GMV), amplitude of low-frequency fluctuation (ALFF), and surface curvature could lead to a maximum accuracy drop of 10.5%. These three categories contributed more than half of the top 10 important features. Besides, removing PRS features caused a modest accuracy drop (8.8%), which was not the least decrease (1.8%) among all feature categories. Conclusions: Our classifier using both MRI and PRS features was stable and not biased to predicting either responder or non-responder. Combining with MRI measures, PRS could provide certain extra predictive power of antipsychotic medication treatment outcomes in schizophrenia. PRS exhibited medium importance in predictions, lower than GMV, ALFF, and surface curvature, but higher than measures of cortical thickness, cortical volume, and surface sulcal depth. Our findings inform the contributions of PRS in predictions of treatment outcomes in schizophrenia.
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Affiliation(s)
- Meng Wang
- Brainnetome Center and National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, Beijing, China.,School of Artificial Intelligence, University of Chinese Academy of Sciences, Beijing, China
| | - Ke Hu
- Brainnetome Center and National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, Beijing, China.,School of Artificial Intelligence, University of Chinese Academy of Sciences, Beijing, China
| | - Lingzhong Fan
- Brainnetome Center and National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, Beijing, China.,School of Artificial Intelligence, University of Chinese Academy of Sciences, Beijing, China.,Center for Excellence in Brain Science and Intelligence Technology, Chinese Academy of Sciences, Shanghai, China
| | - Hao Yan
- Peking University Sixth Hospital/Institute of Mental Health, Beijing, China.,Key Laboratory of Mental Health, Ministry of Health (Peking University), Beijing, China
| | - Peng Li
- Peking University Sixth Hospital/Institute of Mental Health, Beijing, China.,Key Laboratory of Mental Health, Ministry of Health (Peking University), Beijing, China
| | - Tianzi Jiang
- Brainnetome Center and National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, Beijing, China.,School of Artificial Intelligence, University of Chinese Academy of Sciences, Beijing, China.,Center for Excellence in Brain Science and Intelligence Technology, Chinese Academy of Sciences, Shanghai, China.,Key Laboratory for NeuroInformation of Ministry of Education, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, China.,Innovation Academy for Artificial Intelligence, Chinese Academy of Sciences, Beijing, China
| | - Bing Liu
- State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing, China.,Chinese Institute for Brain Research, Beijing, China
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Millard SJ, Bearden CE, Karlsgodt KH, Sharpe MJ. The prediction-error hypothesis of schizophrenia: new data point to circuit-specific changes in dopamine activity. Neuropsychopharmacology 2022; 47:628-640. [PMID: 34588607 PMCID: PMC8782867 DOI: 10.1038/s41386-021-01188-y] [Citation(s) in RCA: 24] [Impact Index Per Article: 12.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/04/2021] [Revised: 08/23/2021] [Accepted: 09/07/2021] [Indexed: 02/07/2023]
Abstract
Schizophrenia is a severe psychiatric disorder affecting 21 million people worldwide. People with schizophrenia suffer from symptoms including psychosis and delusions, apathy, anhedonia, and cognitive deficits. Strikingly, schizophrenia is characterised by a learning paradox involving difficulties learning from rewarding events, whilst simultaneously 'overlearning' about irrelevant or neutral information. While dysfunction in dopaminergic signalling has long been linked to the pathophysiology of schizophrenia, a cohesive framework that accounts for this learning paradox remains elusive. Recently, there has been an explosion of new research investigating how dopamine contributes to reinforcement learning, which illustrates that midbrain dopamine contributes in complex ways to reinforcement learning, not previously envisioned. This new data brings new possibilities for how dopamine signalling contributes to the symptomatology of schizophrenia. Building on recent work, we present a new neural framework for how we might envision specific dopamine circuits contributing to this learning paradox in schizophrenia in the context of models of reinforcement learning. Further, we discuss avenues of preclinical research with the use of cutting-edge neuroscience techniques where aspects of this model may be tested. Ultimately, it is hoped that this review will spur to action more research utilising specific reinforcement learning paradigms in preclinical models of schizophrenia, to reconcile seemingly disparate symptomatology and develop more efficient therapeutics.
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Affiliation(s)
- Samuel J. Millard
- grid.19006.3e0000 0000 9632 6718Department of Psychology, University of California, Los Angeles, CA 90095 USA
| | - Carrie E. Bearden
- grid.19006.3e0000 0000 9632 6718Department of Psychology, University of California, Los Angeles, CA 90095 USA ,grid.19006.3e0000 0000 9632 6718Department of Psychiatry and Biobehavioral Sciences, University of California, Los Angeles, CA 90095 USA
| | - Katherine H. Karlsgodt
- grid.19006.3e0000 0000 9632 6718Department of Psychology, University of California, Los Angeles, CA 90095 USA ,grid.19006.3e0000 0000 9632 6718Department of Psychiatry and Biobehavioral Sciences, University of California, Los Angeles, CA 90095 USA
| | - Melissa J. Sharpe
- grid.19006.3e0000 0000 9632 6718Department of Psychology, University of California, Los Angeles, CA 90095 USA
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