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Zhang D, Xiong Y, Lu H, Duan C, Huang J, Li Y, Bian X, Zhang D, Zhou J, Pan L, Lou X. Predicting tremor improvement after MRgFUS thalamotomy in essential tremor from preoperative spontaneous brain activity: A machine learning approach. Sci Bull (Beijing) 2024:S2095-9273(24)00588-7. [PMID: 39191568 DOI: 10.1016/j.scib.2024.05.049] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/19/2024] [Revised: 05/27/2024] [Accepted: 05/28/2024] [Indexed: 08/29/2024]
Abstract
Magnetic resonance-guided focused ultrasound surgery (MRgFUS) thalamotomy is an emerging technique for medication-refractory essential tremor (ET), but with variable outcomes. This study used pattern regression analysis to identify brain signatures predictive of tremor improvements. Fifty-four ET patients (mean age = 63.06 years, standard deviation (SD) = 10.55 years, 38 males) underwent unilateral MRgFUS thalamotomy and were scanned for resting-state functional magnetic resonance imaging (rs-fMRI). Seventy-four healthy controls (mean age = 58.09 years, SD = 10.30 years, 38 males) were recruited for comparison. Tremor responses at 12 months posttreatment were evaluated by the Clinical Rating Scale for Tremor. The fractional amplitude of low-frequency fluctuations (fALFF) was calculated from rs-fMRI data. Two-sample t-test was used to generate a disease-specific mask, within which Multivariate Kernel Ridge Regression analyses were conducted. Predicted and actual clinical scores were compared using Pearson's correlation coefficient (r) and normalized mean squared error (Norm. MSE). Permutation test and leave-one-out strategy were applied for results validation. KRR identified fALFF patterns that significantly predicted the hand tremor improvement (r = 0.23, P = 0.025; Norm. MSE = 0.05, P = 0.026) and the postural tremor improvement (r = 0.28, P = 0.025; Norm. MSE = 0.06, P = 0.023), but not action tremor improvement. Lobule VI of right cerebellum (Cerebelum_6_R), right superior occipital gyrus (Occipital_Sup_R) and lobule X of vermis (Vermis_10) contributed most for hand tremor prediction (normalized weights (NW): 2.77%, 2.40%, 2.34%) while Vermis_10, left supplementary motor area (Supp_Motor_Area_L) and right hippocampus (Hippocampus_R) for postural tremor prediction (NW: 2.69%, 2.12%, 2.05%). The low contributing NW of the individual brain regions suggested that the fALFF pattern as a whole is an overall predicting feature. Preoperative fALFF pattern predicts tremor benefits induced by MRgFUS thalamotomy. ClinicalTrials.gov number: NCT04570046.
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Affiliation(s)
- Dong Zhang
- Department of Radiology, The First Medical Center, Chinese PLA General Hospital, Beijing 100853, China
| | - Yongqin Xiong
- Department of Radiology, The First Medical Center, Chinese PLA General Hospital, Beijing 100853, China
| | - Haoxuan Lu
- Department of Radiology, The First Medical Center, Chinese PLA General Hospital, Beijing 100853, China
| | - Caohui Duan
- Department of Radiology, The First Medical Center, Chinese PLA General Hospital, Beijing 100853, China
| | - Jiayu Huang
- Department of Radiology, The First Medical Center, Chinese PLA General Hospital, Beijing 100853, China
| | - Yan Li
- Department of Radiology, The First Medical Center, Chinese PLA General Hospital, Beijing 100853, China
| | - Xiangbing Bian
- Department of Radiology, The First Medical Center, Chinese PLA General Hospital, Beijing 100853, China
| | - Dekang Zhang
- Department of Radiology, The First Medical Center, Chinese PLA General Hospital, Beijing 100853, China
| | - Jiayou Zhou
- Department of Neurosurgery, The First Medical Center, Chinese PLA General Hospital, Beijing 100853, China
| | - Longsheng Pan
- Department of Neurosurgery, The First Medical Center, Chinese PLA General Hospital, Beijing 100853, China.
| | - Xin Lou
- Department of Radiology, The First Medical Center, Chinese PLA General Hospital, Beijing 100853, China.
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Mohammad S, Gentreau M, Dubol M, Rukh G, Mwinyi J, Schiöth HB. Association of polygenic scores for autism with volumetric MRI phenotypes in cerebellum and brainstem in adults. Mol Autism 2024; 15:34. [PMID: 39113134 PMCID: PMC11304666 DOI: 10.1186/s13229-024-00611-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/10/2024] [Accepted: 07/22/2024] [Indexed: 08/10/2024] Open
Abstract
Previous research on autism spectrum disorders (ASD) have showed important volumetric alterations in the cerebellum and brainstem. Most of these studies are however limited to case-control studies with small clinical samples and including mainly children or adolescents. Herein, we aimed to explore the association between the cumulative genetic load (polygenic risk score, PRS) for ASD and volumetric alterations in the cerebellum and brainstem, as well as global brain tissue volumes of the brain among adults at the population level. We utilized the latest genome-wide association study of ASD by the Psychiatric Genetics Consortium (18,381 cases, 27,969 controls) and constructed the ASD PRS in an independent cohort, the UK Biobank. Regression analyses controlled for multiple comparisons with the false-discovery rate (FDR) at 5% were performed to investigate the association between ASD PRS and forty-four brain magnetic resonance imaging (MRI) phenotypes among ~ 31,000 participants. Primary analyses included sixteen MRI phenotypes: total volumes of the brain, cerebrospinal fluid (CSF), grey matter (GM), white matter (WM), GM of whole cerebellum, brainstem, and ten regions of the cerebellum (I_IV, V, VI, VIIb, VIIIa, VIIIb, IX, X, CrusI and CrusII). Secondary analyses included twenty-eight MRI phenotypes: the sub-regional volumes of cerebellum including the GM of the vermis and both left and right lobules of each cerebellar region. ASD PRS were significantly associated with the volumes of seven brain areas, whereby higher PRS were associated to reduced volumes of the whole brain, WM, brainstem, and cerebellar regions I-IV, IX, and X, and an increased volume of the CSF. Three sub-regional volumes including the left cerebellar lobule I-IV, cerebellar vermes VIIIb, and X were significantly and negatively associated with ASD PRS. The study highlights a substantial connection between susceptibility to ASD, its underlying genetic etiology, and neuroanatomical alterations of the adult brain.
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Affiliation(s)
- Salahuddin Mohammad
- Functional Pharmacology and Neuroscience Unit, Department of Surgical Sciences, Uppsala University, Uppsala, Sweden
| | - Mélissa Gentreau
- Functional Pharmacology and Neuroscience Unit, Department of Surgical Sciences, Uppsala University, Uppsala, Sweden
| | - Manon Dubol
- Department of Women's and Children's Health, Science for Life Laboratory, Uppsala University, Uppsala, Sweden
| | - Gull Rukh
- Functional Pharmacology and Neuroscience Unit, Department of Surgical Sciences, Uppsala University, Uppsala, Sweden
| | - Jessica Mwinyi
- Functional Pharmacology and Neuroscience Unit, Department of Surgical Sciences, Uppsala University, Uppsala, Sweden
| | - Helgi B Schiöth
- Functional Pharmacology and Neuroscience Unit, Department of Surgical Sciences, Uppsala University, Uppsala, Sweden.
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Jiang X, Zai CC, Dimick MK, Kennedy JL, Young LT, Birmaher B, Goldstein BI. Psychiatric Polygenic Risk Scores Across Youth With Bipolar Disorder, Youth at High Risk for Bipolar Disorder, and Controls. J Am Acad Child Adolesc Psychiatry 2024:S0890-8567(24)00062-5. [PMID: 38340895 DOI: 10.1016/j.jaac.2023.12.009] [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: 07/05/2023] [Revised: 11/23/2023] [Accepted: 02/01/2024] [Indexed: 02/12/2024]
Abstract
OBJECTIVE There is a pronounced gap in knowledge regarding polygenic underpinnings of youth bipolar disorder (BD). This study aimed to compare polygenic risk scores (PRSs) in youth with BD, youth at high clinical and/or familial risk for BD (HR), and controls. METHOD Participants were 344 youths of European ancestry (13-20 years old), including 136 youths with BD, 121 HR youths, and 87 controls. PRSs for BD, schizophrenia, major depressive disorder, and attention-deficit/hyperactivity disorder were constructed using independent genome-wide summary statistics from adult cohorts. Multinomial logistic regression was used to examine the association between each PRS and diagnostic status (BD vs HR vs controls). All genetic analyses controlled for age, sex, and 2 genetic principal components. RESULTS The BD group showed significantly higher BD-PRS than the control group (odds ratio = 1.54, 95% CI = 1.13-2.10, p = .006), with the HR group numerically intermediate. BD-PRS explained 7.9% of phenotypic variance. PRSs for schizophrenia, major depressive disorder, and attention-deficit/hyperactivity disorder were not significantly different among groups. In the BD group, BD-PRS did not significantly differ in relation to BD subtype, age of onset, psychosis, or family history of BD. CONCLUSION BD-PRS derived from adult genome-wide summary statistics is elevated in youth with BD. Absence of significant between-group differences in PRSs for other psychiatric disorders supports the specificity of BD-PRS in youth. These findings add to the biological validation of BD in youth and could have implications for early identification and diagnosis. To enhance clinical utility, future genome-wide association studies that focus specifically on early-onset BD are warranted, as are studies integrating additional genetic and environmental factors. DIVERSITY & INCLUSION STATEMENT We worked to ensure sex and gender balance in the recruitment of human participants. One or more of the authors of this paper self-identifies as a member of one or more historically underrepresented sexual and/or gender groups in science. One or more of the authors of this paper self-identifies as a member of one or more historically underrepresented racial and/or ethnic groups in science. We actively worked to promote sex and gender balance in our author group. We actively worked to promote inclusion of historically underrepresented racial and/or ethnic groups in science in our author group. The author list of this paper includes contributors from the location and/or community where the research was conducted who participated in the data collection, design, analysis, and/or interpretation of the work.
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Affiliation(s)
- Xinyue Jiang
- Centre for Youth Bipolar Disorder, Centre for Addiction and Mental Health, Toronto, Canada; University of Toronto, Toronto, Ontario, Canada
| | - Clement C Zai
- Centre for Youth Bipolar Disorder, Centre for Addiction and Mental Health, Toronto, Canada; University of Toronto, Toronto, Ontario, Canada; Tanenbaum Centre for Pharmacogenetics, Psychiatric Neurogenetics Section, Campbell Family Mental Health Research Institute, Centre for Addiction and Mental Health, Toronto, Ontario, Canada
| | - Mikaela K Dimick
- Centre for Youth Bipolar Disorder, Centre for Addiction and Mental Health, Toronto, Canada
| | - James L Kennedy
- University of Toronto, Toronto, Ontario, Canada; Tanenbaum Centre for Pharmacogenetics, Psychiatric Neurogenetics Section, Campbell Family Mental Health Research Institute, Centre for Addiction and Mental Health, Toronto, Ontario, Canada
| | - L Trevor Young
- University of Toronto, Toronto, Ontario, Canada; Centre for Addiction and Mental Health, Toronto, Ontario, Canada
| | - Boris Birmaher
- Western Psychiatric Hospital, University of Pittsburgh School of Medicine, Pittsburgh, Pennsylvania
| | - Benjamin I Goldstein
- Centre for Youth Bipolar Disorder, Centre for Addiction and Mental Health, Toronto, Canada; University of Toronto, Toronto, Ontario, Canada.
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Chen J, Li T, Zhao B, Chen H, Yuan C, Garden GA, Wu G, Zhu H. The interaction effects of age, APOE and common environmental risk factors on human brain structure. Cereb Cortex 2024; 34:bhad472. [PMID: 38112569 PMCID: PMC10793588 DOI: 10.1093/cercor/bhad472] [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: 05/04/2023] [Revised: 10/09/2023] [Accepted: 11/06/2023] [Indexed: 12/21/2023] Open
Abstract
Mounting evidence suggests considerable diversity in brain aging trajectories, primarily arising from the complex interplay between age, genetic, and environmental risk factors, leading to distinct patterns of micro- and macro-cerebral aging. The underlying mechanisms of such effects still remain unclear. We conducted a comprehensive association analysis between cerebral structural measures and prevalent risk factors, using data from 36,969 UK Biobank subjects aged 44-81. Participants were assessed for brain volume, white matter diffusivity, Apolipoprotein E (APOE) genotypes, polygenic risk scores, lifestyles, and socioeconomic status. We examined genetic and environmental effects and their interactions with age and sex, and identified 726 signals, with education, alcohol, and smoking affecting most brain regions. Our analysis revealed negative age-APOE-ε4 and positive age-APOE-ε2 interaction effects, respectively, especially in females on the volume of amygdala, positive age-sex-APOE-ε4 interaction on the cerebellar volume, positive age-excessive-alcohol interaction effect on the mean diffusivity of the splenium of the corpus callosum, positive age-healthy-diet interaction effect on the paracentral volume, and negative APOE-ε4-moderate-alcohol interaction effects on the axial diffusivity of the superior fronto-occipital fasciculus. These findings highlight the need of considering age, sex, genetic, and environmental joint effects in elucidating normal or abnormal brain aging.
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Affiliation(s)
- Jie Chen
- Department of Biostatistics, University of North Carolina at Chapel Hill, 135 Dauer Drive, Chapel Hill NC 27514, United States
| | - Tengfei Li
- Department of Radiology, School of Medicine, University of North Carolina at Chapel Hill, 101 Manning Drive, Chapel Hill, NC 27514, United States
- Biomedical Research Imaging Center, School of Medicine, University of North Carolina at Chapel Hill, 125 Mason Farm Road, Chapel Hill, NC 27599, United States
| | - Bingxin Zhao
- Department of Statistics and Data Science, The Wharton School, University of Pennsylvania, 265 South 37th Street, 3rd & 4th Floors, Philadelphia, PA 19104-1686, United States
| | - Hui Chen
- School of Public Health, Zhejiang University School of Medicine, 866 Yuhangtang Rd, Hangzhou 310058, China
| | - Changzheng Yuan
- School of Public Health, Zhejiang University School of Medicine, 866 Yuhangtang Rd, Hangzhou 310058, China
- Department of Nutrition, Harvard T H Chan School of Public Health, 665 Huntington Avenue Boston, MA, 02115, United States
| | - Gwenn A Garden
- Department of Neurology, School of Medicine, University of North Carolina at Chapel Hill, 170 Manning Drive Chapel Hill, NC 27599-7025, United States
| | - Guorong Wu
- Department of Psychiatry, School of Medicine, University of North Carolina at Chapel Hill, 101 Manning Drive, Chapel Hill, NC 27514, United States
- Departments of Statistics and Operations Research, University of North Carolina at Chapel Hill, 318 E Cameron Ave #3260, Chapel Hill, NC 27599, United States
- Departments of Computer Science, University of North Carolina at Chapel Hill, 201 South Columbia Street, Chapel Hill, NC 27599, United States
- UNC Neuroscience Center, University of North Carolina at Chapel Hill, 116 Manning Dr, Chapel Hill, NC 27599, United States
- Carolina Institute for Developmental Disabilities, 101 Renee Lynne Ct, Carrboro, NC 27510, United States
| | - Hongtu Zhu
- Department of Biostatistics, University of North Carolina at Chapel Hill, 135 Dauer Drive, Chapel Hill NC 27514, United States
- Biomedical Research Imaging Center, School of Medicine, University of North Carolina at Chapel Hill, 125 Mason Farm Road, Chapel Hill, NC 27599, United States
- Departments of Statistics and Operations Research, University of North Carolina at Chapel Hill, 318 E Cameron Ave #3260, Chapel Hill, NC 27599, United States
- Departments of Computer Science, University of North Carolina at Chapel Hill, 201 South Columbia Street, Chapel Hill, NC 27599, United States
- Departments of Genetics, University of North Carolina at Chapel Hill, 120 Mason Farm Road, Chapel Hill, NC 27514, United States
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Jameei H, Rakesh D, Zalesky A, Cairns MJ, Reay WR, Wray NR, Di Biase MA. Linking Polygenic Risk of Schizophrenia to Variation in Magnetic Resonance Imaging Brain Measures: A Comprehensive Systematic Review. Schizophr Bull 2024; 50:32-46. [PMID: 37354489 PMCID: PMC10754175 DOI: 10.1093/schbul/sbad087] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 06/26/2023]
Abstract
BACKGROUND AND HYPOTHESIS Schizophrenia is highly heritable, with a polygenic effect of many genes conferring risk. Evidence on whether cumulative risk also predicts alterations in brain morphology and function is inconsistent. This systematic review examined evidence for schizophrenia polygenic risk score (sczPRS) associations with commonly used magnetic resonance imaging (MRI) measures. We expected consistent evidence to emerge for significant sczPRS associations with variation in structure and function, specifically in frontal, temporal, and insula cortices that are commonly implicated in schizophrenia pathophysiology. STUDY DESIGN In accordance with Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines, we searched MEDLINE, Embase, and PsycINFO for peer-reviewed studies published between January 2013 and March 2022. Studies were screened against predetermined criteria and National Institutes of Health (NIH) quality assessment tools. STUDY RESULTS In total, 57 studies of T1-weighted structural, diffusion, and functional MRI were included (age range = 9-80 years, Nrange = 64-76 644). We observed moderate, albeit preliminary, evidence for higher sczPRS predicting global reductions in cortical thickness and widespread variation in functional connectivity, and to a lesser extent, region-specific reductions in frontal and temporal volume and thickness. Conversely, sczPRS does not predict whole-brain surface area or gray/white matter volume. Limited evidence emerged for sczPRS associations with diffusion tensor measures of white matter microstructure in a large community sample and smaller cohorts of children and young adults. These findings were broadly consistent across community and clinical populations. CONCLUSIONS Our review supports the hypothesis that schizophrenia is a disorder of disrupted within and between-region brain connectivity, and points to specific whole-brain and regional MRI metrics that may provide useful intermediate phenotypes.
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Affiliation(s)
- Hadis Jameei
- Melbourne Neuropsychiatry Centre, Department of Psychiatry, The University of Melbourne and Melbourne Health, Carlton South, VIC, Australia
| | - Divyangana Rakesh
- Melbourne Neuropsychiatry Centre, Department of Psychiatry, The University of Melbourne and Melbourne Health, Carlton South, VIC, Australia
| | - Andrew Zalesky
- Melbourne Neuropsychiatry Centre, Department of Psychiatry, The University of Melbourne and Melbourne Health, Carlton South, VIC, Australia
- Faculty of Engineering and Information Technology, The University of Melbourne, Parkville, VIC, Australia
| | - Murray J Cairns
- School of Biomedical Sciences and Pharmacy, The University of Newcastle, Newcastle, NSW, Australia
- Precision Medicine Research Program, Hunter Medical Research Institute, Newcastle, NSW, Australia
| | - William R Reay
- School of Biomedical Sciences and Pharmacy, The University of Newcastle, Newcastle, NSW, Australia
- Precision Medicine Research Program, Hunter Medical Research Institute, Newcastle, NSW, Australia
| | - Naomi R Wray
- Institute for Molecular Bioscience, The University of Queensland, Brisbane, QLD, Australia
- Queensland Brain Institute, The University of Queensland, Brisbane, QLD, Australia
| | - Maria A Di Biase
- Melbourne Neuropsychiatry Centre, Department of Psychiatry, The University of Melbourne and Melbourne Health, Carlton South, VIC, Australia
- Department of Anatomy and Physiology, School of Biomedical Sciences, The University of Melbourne, VIC, Australia
- Department of Psychiatry, Brigham and Women’s Hospital, Harvard Medical School, Boston, MA, USA
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Jiang X, Zai CC, Sultan AA, Dimick MK, Nikolova YS, Felsky D, Young LT, MacIntosh BJ, Goldstein BI. Association of polygenic risk for bipolar disorder with resting-state network functional connectivity in youth with and without bipolar disorder. Eur Neuropsychopharmacol 2023; 77:38-52. [PMID: 37717349 DOI: 10.1016/j.euroneuro.2023.08.503] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/04/2023] [Revised: 08/24/2023] [Accepted: 08/29/2023] [Indexed: 09/19/2023]
Abstract
Little is known regarding the polygenic underpinnings of anomalous resting-state functional connectivity (rsFC) in youth bipolar disorder (BD). The current study examined the association of polygenic risk for BD (BD-PRS) with whole-brain rsFC at the large-scale network level in youth with and without BD. 99 youth of European ancestry (56 BD, 43 healthy controls [HC]), ages 13-20 years, completed resting-state fMRI scans. BD-PRS was calculated using summary statistics from the latest adult BD genome-wide association study. Data-driven independent component analyses of the resting-state fMRI data were implemented to examine the association of BD-PRS with rsFC in the overall sample, and separately in BD and HC. In the overall sample, higher BD-PRS was associated with lower rsFC of the salience network and higher rsFC of the frontoparietal network with frontal and parietal regions. Within the BD group, higher BD-PRS was associated with higher rsFC of the default mode network with orbitofrontal cortex, and altered rsFC of the visual network with frontal and occipital regions. Within the HC group, higher BD-PRS was associated with altered rsFC of the frontoparietal network with frontal, temporal and occipital regions. In conclusion, the current study found that BD-PRS generated based on adult genetic data was associated with altered rsFC patterns of brain networks in youth. Our findings support the usefulness of BD-PRS to investigate genetically influenced neuroimaging markers of vulnerability to BD, which can be observed in youth with BD early in their course of illness as well as in healthy youth.
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Affiliation(s)
- Xinyue Jiang
- Centre for Youth Bipolar Disorder, Centre for Addiction and Mental Health, Toronto, ON, Canada; Department of Pharmacology & Toxicology, University of Toronto, Toronto, ON, Canada
| | - Clement C Zai
- Centre for Addiction and Mental Health, Toronto, ON, Canada; Department of Psychiatry, University of Toronto, Toronto, ON, Canada
| | - Alysha A Sultan
- Centre for Youth Bipolar Disorder, Centre for Addiction and Mental Health, Toronto, ON, Canada
| | - Mikaela K Dimick
- Centre for Youth Bipolar Disorder, Centre for Addiction and Mental Health, Toronto, ON, Canada
| | - Yuliya S Nikolova
- Centre for Addiction and Mental Health, Toronto, ON, Canada; Department of Psychiatry, University of Toronto, Toronto, ON, Canada
| | - Daniel Felsky
- Centre for Addiction and Mental Health, Toronto, ON, Canada; Department of Psychiatry, University of Toronto, Toronto, ON, Canada; Division of Biostatistics, Dalla Lana School of Public Health, University of Toronto, Totonto, ON, Canada
| | - L Trevor Young
- Department of Pharmacology & Toxicology, University of Toronto, Toronto, ON, Canada; Centre for Addiction and Mental Health, Toronto, ON, Canada; Department of Psychiatry, University of Toronto, Toronto, ON, Canada
| | - Bradley J MacIntosh
- Sandra E Black Centre for Brain Resilience and Recovery, Sunnybrook Research Institute, Toronto, ON, Canada; Department of Medical Biophysics, University of Toronto, Toronto, ON, Canada; Hurvitz Brain Sciences Program, Sunnybrook Research Institute, Toronto, ON, Canada
| | - Benjamin I Goldstein
- Centre for Youth Bipolar Disorder, Centre for Addiction and Mental Health, Toronto, ON, Canada; Department of Pharmacology & Toxicology, University of Toronto, Toronto, ON, Canada; Department of Psychiatry, University of Toronto, Toronto, ON, Canada.
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Yang Z, Zhao W, Linli Z, Guo S, Feng J. Associations between polygenic risk scores and accelerated brain ageing in smokers. Psychol Med 2023; 53:7785-7794. [PMID: 37555321 PMCID: PMC10755245 DOI: 10.1017/s0033291723001812] [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: 01/10/2023] [Revised: 06/01/2023] [Accepted: 06/05/2023] [Indexed: 08/10/2023]
Abstract
BACKGROUND Smoking contributes to a variety of neurodegenerative diseases and neurobiological abnormalities, suggesting that smoking is associated with accelerated brain aging. However, the neurobiological mechanisms affected by smoking, and whether they are genetically influenced, remain to be investigated. METHODS Using structural magnetic resonance imaging data from the UK Biobank (n = 33 293), a brain age predictor was trained on non-smoking healthy groups and tested on smokers to obtain the BrainAge Gap (BAG). The cumulative effect of multiple common genetic variants associated with smoking was then calculated to acquire a polygenic risk score (PRS). The relationship between PRS, BAG, total gray matter volume (tGMV), and smoking parameters was explored and further genes included in the PRS were annotated to identify potential molecular mechanisms affected by smoking. RESULTS The BrainAge in smokers was predicted with very high accuracy (r = 0.725, MAE = 4.16). Smokers had a greater BAG (Cohen's d = 0.074, p < 0.0001) and higher PRS (Cohen's d = 0.63, p < 0.0001) than non-smokers. A higher PRS was associated with increased amount of smoking, mediated by BAG and tGMV. Several neurotransmitters and ion channel pathways were enriched in the group of smoking-related genes involved in addiction, brain synaptic plasticity, and some neurological disorders. CONCLUSION By using a simplified single indicator of the entire brain (BAG) in combination with the PRS, this study highlights the greater BAG in smokers and its linkage with genes and smoking behavior, providing insight into the neurobiological underpinnings and potential features of smoking-related aging.
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Affiliation(s)
- Zeyu Yang
- MOE-LCSM, School of Mathematics and Statistics, Hunan Normal University, Changsha 410006, P.R.China
- Key Laboratory of Applied Statistics and Data Science, Hunan Normal University, College of Hunan Province, Changsha 410006, P.R.China
| | - Wei Zhao
- MOE-LCSM, School of Mathematics and Statistics, Hunan Normal University, Changsha 410006, P.R.China
- Key Laboratory of Applied Statistics and Data Science, Hunan Normal University, College of Hunan Province, Changsha 410006, P.R.China
| | - Zeqiang Linli
- School of Mathematics and Statistics, Guangdong University of Foreign Studies, Guangzhou, 510006, P.R.China
| | - Shuixia Guo
- MOE-LCSM, School of Mathematics and Statistics, Hunan Normal University, Changsha 410006, P.R.China
- Key Laboratory of Applied Statistics and Data Science, Hunan Normal University, College of Hunan Province, Changsha 410006, P.R.China
| | - Jianfeng Feng
- Centre for Computational Systems Biology, Fudan University, Shanghai 200433, P.R.China
- Department of Computer Science, University of Warwick, Coventry CV4 7AL, England
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Jiang X, Zai CC, Kennedy KG, Zou Y, Nikolova YS, Felsky D, Young LT, MacIntosh BJ, Goldstein BI. Association of polygenic risk for bipolar disorder with grey matter structure and white matter integrity in youth. Transl Psychiatry 2023; 13:322. [PMID: 37852985 PMCID: PMC10584947 DOI: 10.1038/s41398-023-02607-y] [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: 03/23/2023] [Revised: 09/14/2023] [Accepted: 09/20/2023] [Indexed: 10/20/2023] Open
Abstract
There is a gap in knowledge regarding the polygenic underpinnings of brain anomalies observed in youth bipolar disorder (BD). This study examined the association of a polygenic risk score for BD (BD-PRS) with grey matter structure and white matter integrity in youth with and without BD. 113 participants were included in the analyses, including 78 participants with both T1-weighted and diffusion-weighted MRI images, 32 participants with T1-weighted images only, and 3 participants with diffusion-weighted images only. BD-PRS was calculated using PRS-CS-auto and was based on independent adult genome-wide summary statistics. Vertex- and voxel-wise analyses examined the associations of BD-PRS with grey matter metrics (cortical volume [CV], cortical surface area [CSA], cortical thickness [CTh]) and fractional anisotropy [FA] in the combined sample, and separately in BD and HC. In the combined sample of participants with T1-weighted images (n = 110, 66 BD, 44 HC), higher BD-PRS was associated with smaller grey matter metrics in frontal and temporal regions. In within-group analyses, higher BD-PRS was associated with lower CTh of frontal, temporal, and fusiform gyrus in BD, and with lower CV and CSA of superior frontal gyrus in HC. In the combined sample of participants with diffusion-weighted images (n = 81, 49 BD, 32 HC), higher BD-PRS was associated with lower FA in widespread white matter regions. In summary, BD-PRS calculated based on adult genetic data was negatively associated with grey matter structure and FA in youth in regions implicated in BD, which may suggest neuroimaging markers of vulnerability to BD. Future longitudinal studies are needed to examine whether BD-PRS predicts neurodevelopmental changes in BD vs. HC and its interaction with course of illness and long-term medication use.
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Affiliation(s)
- Xinyue Jiang
- Centre for Youth Bipolar Disorder, Centre for Addiction and Mental Health, Toronto, ON, Canada
- Department of Pharmacology & Toxicology, University of Toronto, Toronto, ON, Canada
| | - Clement C Zai
- Centre for Addiction and Mental Health, Toronto, ON, Canada
- Department of Psychiatry, University of Toronto, Toronto, ON, Canada
| | - Kody G Kennedy
- Centre for Youth Bipolar Disorder, Centre for Addiction and Mental Health, Toronto, ON, Canada
| | - Yi Zou
- Centre for Youth Bipolar Disorder, Centre for Addiction and Mental Health, Toronto, ON, Canada
| | - Yuliya S Nikolova
- Centre for Addiction and Mental Health, Toronto, ON, Canada
- Department of Psychiatry, University of Toronto, Toronto, ON, Canada
| | - Daniel Felsky
- Centre for Addiction and Mental Health, Toronto, ON, Canada
- Division of Biostatistics, Dalla Lana School of Public Health, University of Toronto, Toronto, ON, Canada
| | - L Trevor Young
- Department of Pharmacology & Toxicology, University of Toronto, Toronto, ON, Canada
- Centre for Addiction and Mental Health, Toronto, ON, Canada
- Department of Psychiatry, University of Toronto, Toronto, ON, Canada
| | - Bradley J MacIntosh
- Sandra E Black Centre for Brain Resilience and Recovery, Sunnybrook Research Institute, Toronto, ON, Canada
- Department of Medical Biophysics, University of Toronto, Toronto, ON, Canada
- Hurvitz Brain Sciences Program, Sunnybrook Research Institute, Toronto, ON, Canada
| | - Benjamin I Goldstein
- Centre for Youth Bipolar Disorder, Centre for Addiction and Mental Health, Toronto, ON, Canada.
- Department of Pharmacology & Toxicology, University of Toronto, Toronto, ON, Canada.
- Department of Psychiatry, University of Toronto, Toronto, ON, Canada.
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9
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Dimitriadis SI, Perry G, Lancaster TM, Tansey KE, Singh KD, Holmans P, Pocklington A, Davey Smith G, Zammit S, Hall J, O’Donovan MC, Owen MJ, Jones DK, Linden DE. Genetic risk for schizophrenia is associated with increased proportion of indirect connections in brain networks revealed by a semi-metric analysis: evidence from population sample stratified for polygenic risk. Cereb Cortex 2023; 33:2997-3011. [PMID: 35830871 PMCID: PMC10016061 DOI: 10.1093/cercor/bhac256] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/16/2022] [Revised: 05/30/2022] [Accepted: 05/31/2022] [Indexed: 02/02/2023] Open
Abstract
Research studies based on tractography have revealed a prominent reduction of asymmetry in some key white-matter tracts in schizophrenia (SCZ). However, we know little about the influence of common genetic risk factors for SCZ on the efficiency of routing on structural brain networks (SBNs). Here, we use a novel recall-by-genotype approach, where we sample young adults from a population-based cohort (ALSPAC:N genotyped = 8,365) based on their burden of common SCZ risk alleles as defined by polygenic risk score (PRS). We compared 181 individuals at extremes of low (N = 91) or high (N = 90) SCZ-PRS under a robust diffusion MRI-based graph theoretical SBN framework. We applied a semi-metric analysis revealing higher SMR values for the high SCZ-PRS group compared with the low SCZ-PRS group in the left hemisphere. Furthermore, a hemispheric asymmetry index showed a higher leftward preponderance of indirect connections for the high SCZ-PRS group compared with the low SCZ-PRS group (PFDR < 0.05). These findings might indicate less efficient structural connectivity in the higher genetic risk group. This is the first study in a population-based sample that reveals differences in the efficiency of SBNs associated with common genetic risk variants for SCZ.
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Affiliation(s)
- S I Dimitriadis
- Neuroscience and Mental Health Research Institute (NMHI), College of Biomedical and Life Sciences, Cardiff University, Maindy Road CF24 4HQ, Cardiff, Wales, UK
- Cardiff University Brain Research Imaging Centre (CUBRIC), School of Psychology, College of Biomedical and Life Sciences, Cardiff University, Maindy Road CF24 4HQ, Cardiff, Wales, UK
- Division of Psychological Medicine and Clinical Neurosciences, MRC Centre for Neuropsychiatric Genetics and Genomics, Cardiff School of Medicine, Cardiff University, Maindy Road CF24 4HQ, Cardiff, Wales, UK
- Neuroinformatics Group, School of Psychology, College of Biomedical and Life Sciences, Cardiff University, Maindy Road CF24 4HQ, Cardiff, Wales, UK
| | - G Perry
- Cardiff University Brain Research Imaging Centre (CUBRIC), School of Psychology, College of Biomedical and Life Sciences, Cardiff University, Maindy Road CF24 4HQ, Cardiff, Wales, UK
| | - T M Lancaster
- Neuroscience and Mental Health Research Institute (NMHI), College of Biomedical and Life Sciences, Cardiff University, Maindy Road CF24 4HQ, Cardiff, Wales, UK
- Cardiff University Brain Research Imaging Centre (CUBRIC), School of Psychology, College of Biomedical and Life Sciences, Cardiff University, Maindy Road CF24 4HQ, Cardiff, Wales, UK
- Department of Psychology, Bath University, Claverton Down BA2 7AY, Bath, Wales, UK
| | - K E Tansey
- MRC Integrative Epidemiology Unit (IEU), University of Bristol, Queens Road BS8 1QU, Bristol, Wales, UK
| | - K D Singh
- Cardiff University Brain Research Imaging Centre (CUBRIC), School of Psychology, College of Biomedical and Life Sciences, Cardiff University, Maindy Road CF24 4HQ, Cardiff, Wales, UK
| | - P Holmans
- Division of Psychological Medicine and Clinical Neurosciences, MRC Centre for Neuropsychiatric Genetics and Genomics, Cardiff School of Medicine, Cardiff University, Maindy Road CF24 4HQ, Cardiff, Wales, UK
| | - A Pocklington
- Division of Psychological Medicine and Clinical Neurosciences, MRC Centre for Neuropsychiatric Genetics and Genomics, Cardiff School of Medicine, Cardiff University, Maindy Road CF24 4HQ, Cardiff, Wales, UK
| | - G Davey Smith
- MRC Integrative Epidemiology Unit (IEU), University of Bristol, Queens Road BS8 1QU, Bristol, Wales, UK
- Population Health Sciences, Bristol Medical School, University of Bristol, 1-5 Whiteladies Road BS8 1NU, Bristol, Wales, UK
| | - S Zammit
- Division of Psychological Medicine and Clinical Neurosciences, MRC Centre for Neuropsychiatric Genetics and Genomics, Cardiff School of Medicine, Cardiff University, Maindy Road CF24 4HQ, Cardiff, Wales, UK
- Population Health Sciences, Bristol Medical School, University of Bristol, 1-5 Whiteladies Road BS8 1NU, Bristol, Wales, UK
| | - J Hall
- Neuroscience and Mental Health Research Institute (NMHI), College of Biomedical and Life Sciences, Cardiff University, Maindy Road CF24 4HQ, Cardiff, Wales, UK
- Division of Psychological Medicine and Clinical Neurosciences, MRC Centre for Neuropsychiatric Genetics and Genomics, Cardiff School of Medicine, Cardiff University, Maindy Road CF24 4HQ, Cardiff, Wales, UK
| | - M C O’Donovan
- Neuroscience and Mental Health Research Institute (NMHI), College of Biomedical and Life Sciences, Cardiff University, Maindy Road CF24 4HQ, Cardiff, Wales, UK
- Division of Psychological Medicine and Clinical Neurosciences, MRC Centre for Neuropsychiatric Genetics and Genomics, Cardiff School of Medicine, Cardiff University, Maindy Road CF24 4HQ, Cardiff, Wales, UK
| | - M J Owen
- Neuroscience and Mental Health Research Institute (NMHI), College of Biomedical and Life Sciences, Cardiff University, Maindy Road CF24 4HQ, Cardiff, Wales, UK
- Division of Psychological Medicine and Clinical Neurosciences, MRC Centre for Neuropsychiatric Genetics and Genomics, Cardiff School of Medicine, Cardiff University, Maindy Road CF24 4HQ, Cardiff, Wales, UK
| | - D K Jones
- Cardiff University Brain Research Imaging Centre (CUBRIC), School of Psychology, College of Biomedical and Life Sciences, Cardiff University, Maindy Road CF24 4HQ, Cardiff, Wales, UK
| | - D E Linden
- Neuroscience and Mental Health Research Institute (NMHI), College of Biomedical and Life Sciences, Cardiff University, Maindy Road CF24 4HQ, Cardiff, Wales, UK
- Cardiff University Brain Research Imaging Centre (CUBRIC), School of Psychology, College of Biomedical and Life Sciences, Cardiff University, Maindy Road CF24 4HQ, Cardiff, Wales, UK
- Division of Psychological Medicine and Clinical Neurosciences, MRC Centre for Neuropsychiatric Genetics and Genomics, Cardiff School of Medicine, Cardiff University, Maindy Road CF24 4HQ, Cardiff, Wales, UK
- Population Health Sciences, Bristol Medical School, University of Bristol, 1-5 Whiteladies Road BS8 1NU, Bristol, Wales, UK
- School for Mental Health and Neuroscience, Faculty of Health, Medicine and Life Sciences, Maastricht University, Universiteitssingel 40 UNS40 6229 ER, Maastricht, The Netherlands
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10
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Liu S, Smit DJA, Abdellaoui A, van Wingen GA, Verweij KJH. Brain Structure and Function Show Distinct Relations With Genetic Predispositions to Mental Health and Cognition. BIOLOGICAL PSYCHIATRY. COGNITIVE NEUROSCIENCE AND NEUROIMAGING 2023; 8:300-310. [PMID: 35961582 DOI: 10.1016/j.bpsc.2022.08.003] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/08/2022] [Revised: 07/09/2022] [Accepted: 08/01/2022] [Indexed: 10/15/2022]
Abstract
BACKGROUND Mental health and cognitive achievement are partly heritable, highly polygenic, and associated with brain variations in structure and function. However, the underlying neural mechanisms remain unclear. METHODS We investigated the association between genetic predispositions to various mental health and cognitive traits and a large set of structural and functional brain measures from the UK Biobank (N = 36,799). We also applied linkage disequilibrium score regression to estimate the genetic correlations between various traits and brain measures based on genome-wide data. To decompose the complex association patterns, we performed a multivariate partial least squares model of the genetic and imaging modalities. RESULTS The univariate analyses showed that certain traits were related to brain structure (significant genetic correlations with total cortical surface area from rg = -0.101 for smoking initiation to rg = 0.230 for cognitive ability), while other traits were related to brain function (significant genetic correlations with functional connectivity from rg = -0.161 for educational attainment to rg = 0.318 for schizophrenia). The multivariate analysis showed that genetic predispositions to attention-deficit/hyperactivity disorder, smoking initiation, and cognitive traits had stronger associations with brain structure than with brain function, whereas genetic predispositions to most other psychiatric disorders had stronger associations with brain function than with brain structure. CONCLUSIONS These results reveal that genetic predispositions to mental health and cognitive traits have distinct brain profiles.
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Affiliation(s)
- Shu Liu
- Amsterdam Neuroscience, Department of Psychiatry, Amsterdam UMC, University of Amsterdam, Amsterdam, the Netherlands.
| | - Dirk J A Smit
- Amsterdam Neuroscience, Department of Psychiatry, Amsterdam UMC, University of Amsterdam, Amsterdam, the Netherlands
| | - Abdel Abdellaoui
- Amsterdam Neuroscience, Department of Psychiatry, Amsterdam UMC, University of Amsterdam, Amsterdam, the Netherlands
| | - Guido A van Wingen
- Amsterdam Neuroscience, Department of Psychiatry, Amsterdam UMC, University of Amsterdam, Amsterdam, the Netherlands.
| | - Karin J H Verweij
- Amsterdam Neuroscience, Department of Psychiatry, Amsterdam UMC, University of Amsterdam, Amsterdam, the Netherlands
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11
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Wang Z, Liu C, Dong Q, Xue G, Chen C. Polygenic risk score for five major psychiatric disorders associated with volume of distinct brain regions in the general population. Biol Psychol 2023; 178:108530. [PMID: 36858107 DOI: 10.1016/j.biopsycho.2023.108530] [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: 09/08/2022] [Revised: 02/23/2023] [Accepted: 02/25/2023] [Indexed: 03/03/2023]
Abstract
Risk genes and abnormal brain structural indices of psychiatric disorders have been extensively studied. However, whether genetic risk influences brain structure in the general population has been rarely studied. The current study enrolled 483 young Chinese adults, calculated their polygenic risk scores (PRS) for psychiatric disorders based on Psychiatric Genomics Consortium GWAS results, and examined the association between PRSs and brain volume. We found that PRSs were associated with the volume of many brain regions, with differences between PRS for different disorder, calculated at different threshold, and calculated using European or East Asian ancestry. Of them, the PRS for Major Depressive Disorder based on European ancestry was positively associated with right temporal gyrus; the PRS for schizophrenia based on East Asian ancestry was negatively associated with right precentral and postcentral gyrus; the PRS for schizophrenia based on European ancestry was positively associated with right superior temporal gyrus. All these brain regions are critical for corresponding disorders. However, no significant associations were found between PRS for Autism Spectrum Disorder / Bipolar Disorder and brain volume; and the association between PRS for Attention Deficit Hyperactivity Disorder at different thresholds and brain volume was inconsistent. These findings suggest distinct brain mechanisms underlying different psychiatric disorders.
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Affiliation(s)
- Ziyi Wang
- State Key Laboratory of Cognitive Neuroscience and Learning & IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing, China; Experimental School Attached to Haidian Teachers' Training College, Xiangshan Branch, Beijing, China
| | - Chang Liu
- Department of Psychology, Washington State University, Pullman, WA, USA
| | - Qi Dong
- State Key Laboratory of Cognitive Neuroscience and Learning & IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing, China
| | - Gui Xue
- State Key Laboratory of Cognitive Neuroscience and Learning & IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing, China
| | - Chunhui Chen
- State Key Laboratory of Cognitive Neuroscience and Learning & IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing, China; Beijing Key Laboratory of Brain Imaging and Connectomics, Beijing Normal University, Beijing, China.
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12
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Manca R, Pardiñas AF, Venneri A. The neural signatures of psychoses in Alzheimer's disease: a neuroimaging genetics approach. Eur Arch Psychiatry Clin Neurosci 2023; 273:253-267. [PMID: 35727357 PMCID: PMC9957843 DOI: 10.1007/s00406-022-01432-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/11/2021] [Accepted: 05/15/2022] [Indexed: 11/03/2022]
Abstract
Psychoses in Alzheimer's disease (AD) are associated with worse prognosis. Genetic vulnerability for schizophrenia (SCZ) may drive AD-related psychoses, yet its impact on brain constituents is still unknown. This study aimed to investigate the association between polygenic risk scores (PRSs) for SCZ and psychotic experiences (PE) and grey matter (GM) volume in patients with AD with (AD-PS) and without (AD-NP) psychosis. Clinical, genetic and T1-weighted MRI data for 800 participants were extracted from the ADNI database: 203 healthy controls, 121 AD-PS and 476 AD-NP. PRSs were calculated using a Bayesian approach and analysed at ten p-value thresholds. Standard voxel-based morphometry was used to process MRI data. Logistic regression models including both PRSs for SCZ and PE, and an AD-PRS were used to predict psychosis in AD. Associations between PRSs and GM volume were investigated in the whole sample and the three groups independently. Only the AD-PRS predicted psychosis in AD. Inconsistent associations between the SCZ-PRS and PE-PRS and GM volumes were found across groups. The SCZ-PRS was negatively associated with medio-temporal/subcortical volumes and positively with medial/orbitofrontal volumes in the AD-PS group. Only medio-temporal areas were more atrophic in the AD-PS group, while there was no significant correlation between psychosis severity and GM volume. Although not associated with psychoses, the SCZ-PRS was correlated with smaller medio-temporal and larger orbitofrontal volumes in AD-PS. Similar alterations have also been observed in SCZ patients. This finding suggest a possible disconnection between these regions associated with psychoses in more advanced AD.
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Affiliation(s)
- Riccardo Manca
- grid.7728.a0000 0001 0724 6933Department of Life Sciences, Brunel University London, Uxbridge, London, UK
| | - Antonio F. Pardiñas
- grid.5600.30000 0001 0807 5670MRC Centre for Neuropsychiatric Genetics and Genomics, Division of Psychological Medicine and Clinical Neurosciences, School of Medicine, Cardiff University, Cardiff, UK
| | - Annalena Venneri
- Department of Life Sciences, Brunel University London, Uxbridge, London, UK. .,Department of Medicine and Surgery, University of Parma, Parma, Italy.
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13
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Cattarinussi G, Delvecchio G, Sambataro F, Brambilla P. The effect of polygenic risk scores for major depressive disorder, bipolar disorder and schizophrenia on morphological brain measures: A systematic review of the evidence. J Affect Disord 2022; 310:213-222. [PMID: 35533776 DOI: 10.1016/j.jad.2022.05.007] [Citation(s) in RCA: 12] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/17/2021] [Revised: 04/27/2022] [Accepted: 05/04/2022] [Indexed: 01/26/2023]
Abstract
BACKGROUND Major depressive disorder (MDD), bipolar disorder (BD) and schizophrenia (SCZ) share clinical features and genetic bases. Magnetic Resonance Imaging (MRI) studies assessing the effect of polygenic risk score (PRS) for psychiatric disorders on brain structure show heterogeneous results. Therefore, we provided an overview of the existing evidence on the association between PRS for MDD, BD and SCZ and MRI abnormalities in clinical and healthy populations. METHODS A search on PubMed, Web of Science and Scopus was performed to identify the studies exploring the effect of PRS for MDD, BD and SCZ on MRI measures. A total of 25 studies were included (N = 13 on healthy individuals and N = 12 on clinical populations). RESULTS Both in affected and unaffected individuals, PRS for BD and SCZ showed either positive or negative correlations with cortical thickness (CT), mostly involving fronto-temporal areas, whereas PRS for MDD was associated with cortical alterations in prefrontal regions in healthy subjects. LIMITATIONS The heterogeneity in the methods limits the conclusions of this review. CONCLUSIONS Overall the evidence on the effect of PRS for MDD, BD and SCZ on brain is considerably heterogeneous and far to be conclusive. However, from the results emerged that PRS for MDD, BD and SCZ were associated with widespread cortical abnormalities in all the populations explored, suggesting that genetic risk for MDD, BD and SCZ might affect neurodevelopmental processes, resulting in cortical alterations that transcend diagnostic boundaries and seem to be independent from the clinical status.
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Affiliation(s)
- Giulia Cattarinussi
- Department of Neuroscience (DNS), University of Padova, Padua, Italy; Padua Neuroscience Center, University of Padova, Padua, Italy
| | - Giuseppe Delvecchio
- Department of Neurosciences and Mental Health, Fondazione IRCCS Ca' Granda Ospedale Maggiore Policlinico, Milan, Italy.
| | - Fabio Sambataro
- Department of Neuroscience (DNS), University of Padova, Padua, Italy; Padua Neuroscience Center, University of Padova, Padua, Italy
| | - Paolo Brambilla
- Department of Neurosciences and Mental Health, Fondazione IRCCS Ca' Granda Ospedale Maggiore Policlinico, Milan, Italy; Department of Pathophysiology and Transplantation, University of Milan, Milan, Italy
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14
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Improved Multiclassification of Schizophrenia Based on Xgboost and Information Fusion for Small Datasets. COMPUTATIONAL AND MATHEMATICAL METHODS IN MEDICINE 2022; 2022:1581958. [PMID: 35903435 PMCID: PMC9325343 DOI: 10.1155/2022/1581958] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 03/14/2022] [Revised: 06/13/2022] [Accepted: 07/02/2022] [Indexed: 12/03/2022]
Abstract
To improve the performance in multiclass classification for small datasets, a new approach for schizophrenic classification is proposed in the present study. Firstly, the Xgboost classifier is introduced to discriminate the two subtypes of schizophrenia from health controls by analyzing the functional magnetic resonance imaging (fMRI) data, while the gray matter volume (GMV) and amplitude of low-frequency fluctuations (ALFF) are extracted as the features of classifiers. Then, the D-S combination rule of evidence is used to achieve fusion to determine the basic probability assignment based on the output of different classifiers. Finally, the algorithm is applied to classify 38 healthy controls, 16 deficit schizophrenic patients, and 31 nondeficit schizophrenic patients. 10-folds cross-validation method is used to assess classification performance. The results show the proposed algorithm with a sensitivity of 73.89%, which is higher than other classification algorithms, such as supported vector machine (SVM), logistic regression (LR), K-nearest neighbor (KNN) algorithm, random forest (RF), BP neural network (NN), classification and regression tree (CART), naive Bayes classifier (NB), extreme gradient boosting (Xgboost), and deep neural network (DNN). The accuracy of the fusion algorithm is higher than that of classifier based on the GMV or ALFF in the small datasets. The accuracy rate of the improved multiclassification method based on Xgboost and fusion algorithm is higher than that of other machine learning methods, which can further assist the diagnosis of clinical schizophrenia.
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15
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Rodrigue AL, Mathias SR, Knowles EEM, Mollon J, Almasy L, Schultz L, Turner J, Calhoun V, Glahn DC. Specificity of Psychiatric Polygenic Risk Scores and their Effects on Associated Risk Phenotypes. BIOLOGICAL PSYCHIATRY GLOBAL OPEN SCIENCE 2022. [PMID: 37519455 PMCID: PMC10382704 DOI: 10.1016/j.bpsgos.2022.05.008] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/16/2022] Open
Abstract
Background Polygenic risk scores (PRSs) are indices of genetic liability for illness, but their clinical utility for predicting risk for a specific psychiatric disorder is limited. Genetic overlap among disorders and their effects on allied phenotypes may be a possible explanation, but this has been difficult to quantify given focus on singular disorders and/or allied phenotypes. Methods We constructed PRSs for 5 psychiatric disorders (schizophrenia, bipolar disorder, major depressive disorder, autism spectrum disorder, attention-deficit/hyperactivity disorder) and 3 nonpsychiatric control traits (height, type II diabetes, irritable bowel disease) in the UK Biobank (N = 31,616) and quantified associations between PRSs and phenotypes allied with mental illness: behavioral (symptoms, cognition, trauma) and brain measures from magnetic resonance imaging. We then evaluated the extent of specificity among PRSs and their effects on these allied phenotypes. Results Correlations among psychiatric PRSs replicated previous work, with overlap between schizophrenia and bipolar disorder, which was distinct from overlap between autism spectrum disorder and attention-deficit/hyperactivity disorder; overlap between psychiatric and control PRSs was minimal. There was, however, substantial overlap of PRS effects on allied phenotypes among psychiatric disorders and among psychiatric disorders and control traits, where the extent and pattern of overlap was phenotype specific. Conclusions Results show that genetic distinctions between psychiatric disorders and between psychiatric disorders and control traits exist, but this does not extend to their effects on allied phenotypes. Although overlap can be informative, work is needed to construct PRSs that will function at the level of specificity needed for clinical application.
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16
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Kang J, Jiao Z, Qin Y, Wang Y, Wang J, Jin L, Feng J, Wang F, Tang Y, Gong X. Associations between polygenic risk scores and amplitude of low-frequency fluctuation of inferior frontal gyrus in schizophrenia. J Psychiatr Res 2022; 147:4-12. [PMID: 34999338 DOI: 10.1016/j.jpsychires.2021.12.043] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/20/2021] [Revised: 12/14/2021] [Accepted: 12/20/2021] [Indexed: 10/19/2022]
Abstract
Schizophrenia (SCZ) is a serious and complex mental disorder with high heritability. Polygenic risk score (PRS) is a useful tool calculating the accumulating effects of multiple common genetic variants of schizophrenia. The amplitude of low-frequency fluctuation (ALFF) is an efficient index to reflect spontaneous, intrinsic neuronal activity. Aberrant ALFF of brain regions were reported in schizophrenia frequently, but the relationship between PRS and ALFF has not been studied. In the present study, we compared PRS and ALFF in 101 schizophrenia patients and 106 age-matched healthy controls to test their associations with schizophrenia. Then, the correlation of PRS with ALFF was measured to reveal the effect of polygenic risk on brain activity in schizophrenia. We found that schizophrenia patients showed significant differences in PRS and ALFF compared with controls. Twenty-six brain regions showed significant difference of ALFF between schizophrenia cases and controls, of which left inferior frontal gyrus, triangular part (IFGtriang.L) showed increased activity in schizophrenia. PRS-SCZ was positively correlated with ALFF in IFGtriang.L in 57 non-chronic patients. Genes involved in synaptic organization and transmission, especially in glutamatergic synapse, were highly enriched in PRS-SCZ genes, suggesting the dysfunction of synapses in schizophrenia. These results help to understand the molecular mechanism underlying schizophrenia and related brain dysfunction.
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Affiliation(s)
- Jujiao Kang
- Institute of Science and Technology for Brain-Inspired Intelligence, Fudan University, Shanghai, China; Shanghai Center for Mathematical Science, Fudan University, Shanghai, China
| | - Zeyu Jiao
- Institute of Science and Technology for Brain-Inspired Intelligence, Fudan University, Shanghai, China; Shanghai Center for Mathematical Science, Fudan University, Shanghai, China
| | - Yue Qin
- School of Life Sciences, Fudan University, Shanghai, China
| | - Yi Wang
- School of Life Sciences, Fudan University, Shanghai, China
| | - Jiucun Wang
- School of Life Sciences, Fudan University, Shanghai, China; Human Phoneme Institute, Fudan University, Shanghai, China
| | - Li Jin
- School of Life Sciences, Fudan University, Shanghai, China
| | - Jianfeng Feng
- Institute of Science and Technology for Brain-Inspired Intelligence, Fudan University, Shanghai, China; Shanghai Center for Mathematical Science, Fudan University, Shanghai, China; Department of Computer Science, University of Warwick, Coventry, CV4 7AL, UK
| | - Fei Wang
- Department of Psychiatry, Affiliated Nanjing Brain Hospital, Nanjing Medical University, Nanjing, China
| | - Yanqing Tang
- Department of Psychiatry, The First Affiliated Hospital of China Medical University, China.
| | - Xiaohong Gong
- School of Life Sciences, Fudan University, Shanghai, China.
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17
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Wei Y, de Lange SC, Pijnenburg R, Scholtens LH, Ardesch DJ, Watanabe K, Posthuma D, van den Heuvel MP. Statistical testing in transcriptomic-neuroimaging studies: A how-to and evaluation of methods assessing spatial and gene specificity. Hum Brain Mapp 2021; 43:885-901. [PMID: 34862695 PMCID: PMC8764473 DOI: 10.1002/hbm.25711] [Citation(s) in RCA: 18] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/03/2021] [Revised: 10/22/2021] [Accepted: 10/23/2021] [Indexed: 11/14/2022] Open
Abstract
Multiscale integration of gene transcriptomic and neuroimaging data is becoming a widely used approach for exploring the molecular underpinnings of large‐scale brain organization in health and disease. Proper statistical evaluation of determined associations between imaging‐based phenotypic and transcriptomic data is key in these explorations, in particular to establish whether observed associations exceed “chance level” of random, nonspecific effects. Recent approaches have shown the importance of statistical models that can correct for spatial autocorrelation effects in the data to avoid inflation of reported statistics. Here, we discuss the need for examination of a second category of statistical models in transcriptomic‐neuroimaging analyses, namely those that can provide “gene specificity.” By means of a couple of simple examples of commonly performed transcriptomic‐neuroimaging analyses, we illustrate some of the potentials and challenges of transcriptomic‐imaging analyses, showing that providing gene specificity on observed transcriptomic‐neuroimaging effects is of high importance to avoid reports of nonspecific effects. Through means of simulations we show that the rate of reported nonspecific effects (i.e., effects that cannot be specifically linked to a specific gene or gene‐set) can run as high as 60%, with only less than 5% of transcriptomic‐neuroimaging associations observed through ordinary linear regression analyses showing both spatial and gene specificity. We provide a discussion, a tutorial, and an easy‐to‐use toolbox for the different options of null models in transcriptomic‐neuroimaging analyses.
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Affiliation(s)
- Yongbin Wei
- Department of Complex Trait Genetics, Center for Neurogenomics and Cognitive Research, Amsterdam Neuroscience, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands
| | - Siemon C de Lange
- Department of Complex Trait Genetics, Center for Neurogenomics and Cognitive Research, Amsterdam Neuroscience, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands.,Department of Sleep and Cognition, Netherlands Institute for Neuroscience (NIN), Royal Netherlands Academy of Arts and Sciences, Amsterdam, The Netherlands
| | - Rory Pijnenburg
- Department of Complex Trait Genetics, Center for Neurogenomics and Cognitive Research, Amsterdam Neuroscience, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands
| | - Lianne H Scholtens
- Department of Complex Trait Genetics, Center for Neurogenomics and Cognitive Research, Amsterdam Neuroscience, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands
| | - Dirk Jan Ardesch
- Department of Complex Trait Genetics, Center for Neurogenomics and Cognitive Research, Amsterdam Neuroscience, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands
| | - Kyoko Watanabe
- Department of Complex Trait Genetics, Center for Neurogenomics and Cognitive Research, Amsterdam Neuroscience, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands
| | - Danielle Posthuma
- Department of Complex Trait Genetics, Center for Neurogenomics and Cognitive Research, Amsterdam Neuroscience, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands.,Department of Child and Adolescent Psychiatry and Psychology, Section Complex Trait Genetics, Amsterdam Neuroscience, Vrije Universiteit Medical Center, Amsterdam UMC, Amsterdam, The Netherlands
| | - Martijn P van den Heuvel
- Department of Complex Trait Genetics, Center for Neurogenomics and Cognitive Research, Amsterdam Neuroscience, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands.,Department of Child and Adolescent Psychiatry and Psychology, Section Complex Trait Genetics, Amsterdam Neuroscience, Vrije Universiteit Medical Center, Amsterdam UMC, Amsterdam, The Netherlands
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18
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Sha Z, Schijven D, Francks C. Patterns of brain asymmetry associated with polygenic risks for autism and schizophrenia implicate language and executive functions but not brain masculinization. Mol Psychiatry 2021; 26:7652-7660. [PMID: 34211121 PMCID: PMC8872997 DOI: 10.1038/s41380-021-01204-z] [Citation(s) in RCA: 16] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/19/2021] [Revised: 06/11/2021] [Accepted: 06/18/2021] [Indexed: 02/06/2023]
Abstract
Autism spectrum disorder (ASD) and schizophrenia have been conceived as partly opposing disorders in terms of systemizing vs. empathizing cognitive styles, with resemblances to male vs. female average sex differences. Left-right asymmetry of the brain is an important aspect of its organization that shows average differences between the sexes and can be altered in both ASD and schizophrenia. Here we mapped multivariate associations of polygenic risk scores for ASD and schizophrenia with asymmetries of regional cerebral cortical surface area, thickness, and subcortical volume measures in 32,256 participants from the UK Biobank. Polygenic risks for the two disorders were positively correlated (r = 0.08, p = 7.13 × 10-50) and both were higher in females compared to males, consistent with biased participation against higher-risk males. Each polygenic risk score was associated with multivariate brain asymmetry after adjusting for sex, ASD r = 0.03, p = 2.17 × 10-9, and schizophrenia r = 0.04, p = 2.61 × 10-11, but the multivariate patterns were mostly distinct for the two polygenic risks and neither resembled average sex differences. Annotation based on meta-analyzed functional imaging data showed that both polygenic risks were associated with asymmetries of regions important for language and executive functions, consistent with behavioral associations that arose in phenome-wide association analysis. Overall, the results indicate that distinct patterns of subtly altered brain asymmetry may be functionally relevant manifestations of polygenic risks for ASD and schizophrenia, but do not support brain masculinization or feminization in their etiologies.
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Affiliation(s)
- Zhiqiang Sha
- Language and Genetics Department, Max Planck Institute for Psycholinguistics, Nijmegen, The Netherlands
| | - Dick Schijven
- Language and Genetics Department, Max Planck Institute for Psycholinguistics, Nijmegen, The Netherlands
| | - Clyde Francks
- Language and Genetics Department, Max Planck Institute for Psycholinguistics, Nijmegen, The Netherlands.
- Donders Institute for Brain, Cognition and Behaviour, Radboud University, Nijmegen, The Netherlands.
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19
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Rootes-Murdy K, Zendehrouh E, Calhoun VD, Turner JA. Spatially Covarying Patterns of Gray Matter Volume and Concentration Highlight Distinct Regions in Schizophrenia. Front Neurosci 2021; 15:708387. [PMID: 34720851 PMCID: PMC8551386 DOI: 10.3389/fnins.2021.708387] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/11/2021] [Accepted: 09/20/2021] [Indexed: 11/29/2022] Open
Abstract
Introduction: Individuals with schizophrenia have consistent gray matter reduction throughout the cortex when compared to healthy individuals. However, the reduction patterns vary based on the quantity (concentration or volume) utilized by study. The objective of this study was to identify commonalities between gray matter concentration and gray matter volume effects in schizophrenia. Methods: We performed both univariate and multivariate analyses of case/control effects on 145 gray matter images from 66 participants with schizophrenia and 79 healthy controls, and processed to compare the concentration and volume estimates. Results: Diagnosis effects in the univariate analysis showed similar areas of volume and concentration reductions in the insula, occipitotemporal gyrus, temporopolar area, and fusiform gyrus. In the multivariate analysis, healthy controls had greater gray matter volume and concentration additionally in the superior temporal gyrus, prefrontal cortex, cerebellum, calcarine, and thalamus. In the univariate analyses there was moderate overlap between gray matter concentration and volume across the entire cortex (r = 0.56, p = 0.02). The multivariate analyses revealed only low overlap across most brain patterns, with the largest correlation (r = 0.37) found in the cerebellum and vermis. Conclusions: Individuals with schizophrenia showed reduced gray matter volume and concentration in previously identified areas of the prefrontal cortex, cerebellum, and thalamus. However, there were only moderate correlations across the cortex when examining the different gray matter quantities. Although these two quantities are related, concentration and volume do not show identical results, and therefore, should not be used interchangeably in the literature.
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Affiliation(s)
- Kelly Rootes-Murdy
- Department of Psychology, Georgia State University, Atlanta, GA, United States.,Neuroscience Institute, Georgia State University, Atlanta, GA, United States
| | - Elaheh Zendehrouh
- Department of Computer Science, Georgia State University, Atlanta, GA, United States.,Tri-Institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS), Georgia State University, Georgia Institute of Technology, Emory University, Atlanta, GA, United States
| | - Vince D Calhoun
- Department of Psychology, Georgia State University, Atlanta, GA, United States.,Neuroscience Institute, Georgia State University, Atlanta, GA, United States.,Tri-Institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS), Georgia State University, Georgia Institute of Technology, Emory University, Atlanta, GA, United States
| | - Jessica A Turner
- Department of Psychology, Georgia State University, Atlanta, GA, United States.,Neuroscience Institute, Georgia State University, Atlanta, GA, United States.,Tri-Institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS), Georgia State University, Georgia Institute of Technology, Emory University, Atlanta, GA, United States
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20
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Rahaman MA, Rodrigue A, Glahn D, Turner J, Calhoun V. Shared sets of correlated polygenic risk scores and voxel-wise grey matter across multiple traits identified via bi-clustering. ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. ANNUAL INTERNATIONAL CONFERENCE 2021; 2021:2201-2206. [PMID: 34891724 DOI: 10.1109/embc46164.2021.9630825] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/14/2023]
Abstract
Neuropsychiatric disorders involve complex polygenic determinants as well as brain alterations. The combination of genetic inheritance and neuroimaging approaches could advance our understanding of psychiatric disorders. However, cross-disorder overlap is a current issue since psychiatric conditions share some neurogenetic correlates, symptoms, and brain effects. Exploring the impact of genetic risk on the brain across disorders could help understand commonalities across multiple psychopathologies. To do this, we first compute the linear relationship between PRS and voxel-wise grey matter volume to generate brain maps for five psychiatric and three control traits. Next, we use the biclustering approach to identify regions of the brain associated with polygenic risk scores in one or more traits. Our results demonstrate a significant overlap in brain regions connected to polygenic risk across psychiatric traits. Moreover, such brain domains are highly allied with the polygenic risk for non-psychiatric control traits. This multi-trait overlap characterizes the nonspecific relationship between neural anatomy and inherited risk factors in psychiatric conditions, and in some cases, the overlap in neural features linked to genetic risk for non-psychiatric attributes.Clinical Relevance-This study presents biclusters of multiple psychiatric and control traits. The analysis reported various brain regions, including cerebellum, cuneus, precuneus, fusiform, supplementary motor area, that show significant correlation with polygenic risk scores across diverse groups of psychiatric conditions and non-psychiatric control traits.
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21
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Janiri D, Kotzalidis GD, di Luzio M, Giuseppin G, Simonetti A, Janiri L, Sani G. Genetic neuroimaging of bipolar disorder: a systematic 2017-2020 update. Psychiatr Genet 2021; 31:50-64. [PMID: 33492063 DOI: 10.1097/ypg.0000000000000274] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/16/2022]
Abstract
There is evidence of genetic polymorphism influences on brain structure and function, genetic risk in bipolar disorder (BD), and neuroimaging correlates of BD. How genetic influences related to BD could be reflected on brain changes in BD has been efficiently reviewed in a 2017 systematic review. We aimed to confirm and extend these findings through a Preferred Reporting Items for Systematic reviews and Meta-Analyses-based systematic review. Our study allowed us to conclude that there is no replicated finding in the timeframe considered. We were also unable to further confirm prior results of the BDNF gene polymorphisms to affect brain structure and function in BD. The most consistent finding is an influence of the CACNA1C rs1006737 polymorphism in brain connectivity and grey matter structure and function. There was a tendency of undersized studies to obtain positive results and large, genome-wide polygenic risk studies to find negative results in BD. The neuroimaging genetics in BD field is rapidly expanding.
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Affiliation(s)
- Delfina Janiri
- Department of Neurology, Fondazione Policlinico Universitario Agostino Gemelli IRCCS
- Department of Psychiatry and Neurology, Sapienza University of Rome
| | - Georgios D Kotzalidis
- NESMOS Department, Sant'Andrea University Hospital, School of Medicine and Psychology, Sapienza University
| | - Michelangelo di Luzio
- Department of Neuroscience, Section of Psychiatry, Università Cattolica del Sacro Cuore, Rome, Italy
| | - Giulia Giuseppin
- Department of Neuroscience, Section of Psychiatry, Università Cattolica del Sacro Cuore, Rome, Italy
| | - Alessio Simonetti
- Department of Psychiatry and Neurology, Sapienza University of Rome
- Menninger Department of Psychiatry and Behavioral Sciences, Baylor College of Medicine, Houston, Texas, USA
| | - Luigi Janiri
- Department of Neuroscience, Section of Psychiatry, Università Cattolica del Sacro Cuore, Rome, Italy
- Department of Psychiatry, Fondazione Policlinico Universitario Agostino Gemelli IRCCS, Rome, Italy
| | - Gabriele Sani
- Department of Neuroscience, Section of Psychiatry, Università Cattolica del Sacro Cuore, Rome, Italy
- Department of Psychiatry, Fondazione Policlinico Universitario Agostino Gemelli IRCCS, Rome, Italy
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22
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Qin Y, Kang J, Jiao Z, Wang Y, Wang J, Wang H, Feng J, Jin L, Wang F, Gong X. Polygenic risk for autism spectrum disorder affects left amygdala activity and negative emotion in schizophrenia. Transl Psychiatry 2020; 10:322. [PMID: 32958750 PMCID: PMC7506524 DOI: 10.1038/s41398-020-01001-2] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/07/2020] [Revised: 09/02/2020] [Accepted: 09/07/2020] [Indexed: 12/27/2022] Open
Abstract
Although the diagnoses based on phenomenology have many practical advantages, accumulating evidence shows that schizophrenia and autism spectrum disorder (ASD) share some overlap in genetics and clinical presentation. It remains largely unknown how ASD-associated polygenetic risk contributes to the pathogenesis of schizophrenia. In the present study, we calculated high-resolution ASD polygenic risk scores (ASD PRSs) and selected optimal ten ASD PRS with minimal P values in the association analysis of PRSs, with schizophrenia to assess the effect of ASD PRS on brain neural activity in schizophrenia cases and controls. We found that amplitude of low-frequency fluctuation in left amygdala was positively associated with ASD PRSs in our cohort. Correlation analysis of ASD PRSs with facial emotion recognition test identified the negative correlation of ASD PRSs with negative emotions in schizophrenia cases and controls. Finally, functional enrichment analysis of PRS genes revealed that neural system function and development, as well as signal transduction, were mainly enriched in PRS genes. Our results provide empirical evidence that polygenic risk for ASD contributes to schizophrenia by the intermediate phenotypes of left amygdala function and emotion recognition. It provides a promising strategy to understand the relationship between phenotypes and genotypes shared in mental disorders.
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Affiliation(s)
- Yue Qin
- grid.8547.e0000 0001 0125 2443State Key Laboratory of Genetic Engineering, School of Life Sciences, Fudan University, Shanghai, China
| | - Jujiao Kang
- grid.8547.e0000 0001 0125 2443Shanghai Center for Mathematical Science, Fudan University, Shanghai, China
| | - Zeyu Jiao
- grid.8547.e0000 0001 0125 2443Shanghai Center for Mathematical Science, Fudan University, Shanghai, China
| | - Yi Wang
- grid.8547.e0000 0001 0125 2443State Key Laboratory of Genetic Engineering, School of Life Sciences, Fudan University, Shanghai, China
| | - Jiucun Wang
- grid.8547.e0000 0001 0125 2443State Key Laboratory of Genetic Engineering, School of Life Sciences, Fudan University, Shanghai, China ,grid.8547.e0000 0001 0125 2443Human Phoneme Institute, Fudan University, Shanghai, China
| | - Hongyan Wang
- grid.8547.e0000 0001 0125 2443State Key Laboratory of Genetic Engineering, School of Life Sciences, Fudan University, Shanghai, China ,grid.8547.e0000 0001 0125 2443Obstetrics and Gynecology Hospital, Fudan University, Shanghai, China
| | - Jianfeng Feng
- grid.8547.e0000 0001 0125 2443Shanghai Center for Mathematical Science, Fudan University, Shanghai, China ,grid.8547.e0000 0001 0125 2443Institute of Science and Technology for Brain-inspired Intelligence, Fudan University, Shanghai, China ,grid.7372.10000 0000 8809 1613Department of Computer Science, University of Warwick, Coventry, CV4 7AL UK
| | - Li Jin
- grid.8547.e0000 0001 0125 2443State Key Laboratory of Genetic Engineering, School of Life Sciences, Fudan University, Shanghai, China
| | - Fei Wang
- grid.412636.4Department of Radiology, The First Affiliated Hospital of China Medical University, Shenyang, China
| | - Xiaohong Gong
- State Key Laboratory of Genetic Engineering, School of Life Sciences, Fudan University, Shanghai, China.
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23
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Taylor JA, Larsen KM, Garrido MI. Multi-dimensional predictions of psychotic symptoms via machine learning. Hum Brain Mapp 2020; 41:5151-5163. [PMID: 32870535 PMCID: PMC7670649 DOI: 10.1002/hbm.25181] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/08/2020] [Revised: 07/09/2020] [Accepted: 08/09/2020] [Indexed: 11/10/2022] Open
Abstract
The diagnostic criteria for schizophrenia comprise a diverse range of heterogeneous symptoms. As a result, individuals each present a distinct set of symptoms despite having the same overall diagnosis. Whilst previous machine learning studies have primarily focused on dichotomous patient-control classification, we predict the severity of each individual symptom on a continuum. We applied machine learning regression within a multi-modal fusion framework to fMRI and behavioural data acquired during an auditory oddball task in 80 schizophrenia patients. Brain activity was highly predictive of some, but not all symptoms, namely hallucinations, avolition, anhedonia and attention. Critically, each of these symptoms was associated with specific functional alterations across different brain regions. We also found that modelling symptoms as an ensemble of subscales was more accurate, specific and informative than models which predict compound scores directly. In principle, this approach is transferrable to any psychiatric condition or multi-dimensional diagnosis.
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Affiliation(s)
- Jeremy A Taylor
- Melbourne School of Psychological Sciences, University of Melbourne, Parkville, Victoria, Australia.,Queensland Brain Institute, University of Queensland, St Lucia, Queensland, Australia
| | - Kit M Larsen
- Queensland Brain Institute, University of Queensland, St Lucia, Queensland, Australia.,Australian Research Council Centre of Excellence for Integrative Brain Function, Clayton, Victoria, Australia.,Danish Research Centre for Magnetic Resonance, Centre for Functional and Diagnostic Imaging and Research, Copenhagen University Hospital Hvidovre, Hvidovre, Denmark.,Child and Adolescent Mental Health Care, Mental Health Services Capital Region Copenhagen, University of Copenhagen, Copenhagen, Denmark
| | - Marta I Garrido
- Melbourne School of Psychological Sciences, University of Melbourne, Parkville, Victoria, Australia.,Queensland Brain Institute, University of Queensland, St Lucia, Queensland, Australia.,Australian Research Council Centre of Excellence for Integrative Brain Function, Clayton, Victoria, Australia.,Centre for Advanced Imaging, University of Queensland, St Lucia, Queensland, Australia
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24
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Wendt FR, Pathak GA, Tylee DS, Goswami A, Polimanti R. Heterogeneity and Polygenicity in Psychiatric Disorders: A Genome-Wide Perspective. ACTA ACUST UNITED AC 2020; 4:2470547020924844. [PMID: 32518889 PMCID: PMC7254587 DOI: 10.1177/2470547020924844] [Citation(s) in RCA: 22] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/03/2020] [Accepted: 04/17/2020] [Indexed: 12/15/2022]
Abstract
Genome-wide association studies (GWAS) have been performed for many psychiatric disorders and revealed a complex polygenic architecture linking mental and physical health phenotypes. Psychiatric diagnoses are often heterogeneous, and several layers of trait heterogeneity may contribute to detection of genetic risks per disorder or across multiple disorders. In this review, we discuss these heterogeneities and their consequences on the discovery of risk loci using large-scale genetic data. We primarily highlight the ways in which sex and diagnostic complexity contribute to risk locus discovery in schizophrenia, bipolar disorder, attention deficit hyperactivity disorder, autism spectrum disorder, posttraumatic stress disorder, major depressive disorder, obsessive-compulsive disorder, Tourette’s syndrome and chronic tic disorder, anxiety disorders, suicidality, feeding and eating disorders, and substance use disorders. Genetic data also have facilitated discovery of clinically relevant subphenotypes also described here. Collectively, GWAS of psychiatric disorders revealed that the understanding of heterogeneity, polygenicity, and pleiotropy is critical to translate genetic findings into treatment strategies.
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Affiliation(s)
- Frank R Wendt
- Department of Psychiatry, Yale School of Medicine and VA CT Healthcare Center, West Haven, CT, USA
| | - Gita A Pathak
- Department of Psychiatry, Yale School of Medicine and VA CT Healthcare Center, West Haven, CT, USA
| | - Daniel S Tylee
- Department of Psychiatry, Yale School of Medicine and VA CT Healthcare Center, West Haven, CT, USA
| | - Aranyak Goswami
- Department of Psychiatry, Yale School of Medicine and VA CT Healthcare Center, West Haven, CT, USA
| | - Renato Polimanti
- Department of Psychiatry, Yale School of Medicine and VA CT Healthcare Center, West Haven, CT, USA
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25
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Nicholson AA, Harricharan S, Densmore M, Neufeld RWJ, Ros T, McKinnon MC, Frewen PA, Théberge J, Jetly R, Pedlar D, Lanius RA. Classifying heterogeneous presentations of PTSD via the default mode, central executive, and salience networks with machine learning. Neuroimage Clin 2020; 27:102262. [PMID: 32446241 PMCID: PMC7240193 DOI: 10.1016/j.nicl.2020.102262] [Citation(s) in RCA: 40] [Impact Index Per Article: 10.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/23/2019] [Revised: 04/15/2020] [Accepted: 04/16/2020] [Indexed: 01/26/2023]
Abstract
Intrinsic connectivity networks (ICNs), including the default mode network (DMN), the central executive network (CEN), and the salience network (SN) have been shown to be aberrant in patients with posttraumatic stress disorder (PTSD). The purpose of the current study was to a) compare ICN functional connectivity between PTSD, dissociative subtype PTSD (PTSD+DS) and healthy individuals; and b) to examine the use of multivariate machine learning algorithms in classifying PTSD, PTSD+DS, and healthy individuals based on ICN functional activation. Our neuroimaging dataset consisted of resting-state fMRI scans from 186 participants [PTSD (n = 81); PTSD + DS (n = 49); and healthy controls (n = 56)]. We performed group-level independent component analyses to evaluate functional connectivity differences within each ICN. Multiclass Gaussian Process Classification algorithms within PRoNTo software were then used to predict the diagnosis of PTSD, PTSD+DS, and healthy individuals based on ICN functional activation. When comparing the functional connectivity of ICNs between PTSD, PTSD+DS and healthy controls, we found differential patterns of connectivity to brain regions involved in emotion regulation, in addition to limbic structures and areas involved in self-referential processing, interoception, bodily self-consciousness, and depersonalization/derealization. Machine learning algorithms were able to predict with high accuracy the classification of PTSD, PTSD+DS, and healthy individuals based on ICN functional activation. Our results suggest that alterations within intrinsic connectivity networks may underlie unique psychopathology and symptom presentation among PTSD subtypes. Furthermore, the current findings substantiate the use of machine learning algorithms for classifying subtypes of PTSD illness based on ICNs.
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Affiliation(s)
- Andrew A Nicholson
- Department of Cognition, Emotion and Methods in Psychology, University of Vienna, Austria; Department of Psychiatry and Behavioural Neurosciences, McMaster University, Hamilton, ON, Canada.
| | - Sherain Harricharan
- Department of Psychiatry and Behavioural Neurosciences, McMaster University, Hamilton, ON, Canada
| | - Maria Densmore
- Department of Psychiatry, Western University, London, ON, Canada; Imaging Division, Lawson Health Research Institute, London, ON, Canada
| | - Richard W J Neufeld
- Department of Psychiatry, Western University, London, ON, Canada; Department of Psychology, Western University, London, ON, Canada; Department of Medical Imaging, Western University, London, ON, Canada
| | - Tomas Ros
- Department of Neuroscience, University of Geneva, Switzerland
| | - Margaret C McKinnon
- Department of Psychiatry and Behavioural Neurosciences, McMaster University, Hamilton, ON, Canada; Mood Disorders Program, St. Joseph's Healthcare, Hamilton, ON, Canada; Homewood Research Institute, Guelph, ON, Canada
| | - Paul A Frewen
- Department of Psychiatry, Western University, London, ON, Canada; Department of Neuroscience, Western University, London, ON, Canada
| | - Jean Théberge
- Department of Psychiatry, Western University, London, ON, Canada; Department of Medical Imaging, Western University, London, ON, Canada; Imaging Division, Lawson Health Research Institute, London, ON, Canada; Department of Diagnostic Imaging, St. Joseph's Health Care, London, ON, Canada
| | - Rakesh Jetly
- Canadian Forces, Health Services, Ottawa, Ontario, Canada
| | - David Pedlar
- Canadian Institute for Military and Veteran Health Research (CIMVHR), Canada
| | - Ruth A Lanius
- Department of Psychiatry, Western University, London, ON, Canada; Department of Neuroscience, Western University, London, ON, Canada; Imaging Division, Lawson Health Research Institute, London, ON, Canada
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26
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Maglanoc LA, Kaufmann T, van der Meer D, Marquand AF, Wolfers T, Jonassen R, Hilland E, Andreassen OA, Landrø NI, Westlye LT. Brain Connectome Mapping of Complex Human Traits and Their Polygenic Architecture Using Machine Learning. Biol Psychiatry 2020; 87:717-726. [PMID: 31858985 DOI: 10.1016/j.biopsych.2019.10.011] [Citation(s) in RCA: 18] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/01/2019] [Revised: 10/07/2019] [Accepted: 10/18/2019] [Indexed: 12/14/2022]
Abstract
BACKGROUND Mental disorders and individual characteristics such as intelligence and personality are complex traits sharing a largely unknown neuronal basis. Their genetic architectures are highly polygenic and overlapping, which is supported by heterogeneous phenotypic expression and substantial clinical overlap. Brain network analysis provides a noninvasive means of dissecting biological heterogeneity, yet its sensitivity, specificity, and validity in assessing individual characteristics relevant for brain function and mental health and their genetic underpinnings in clinical applications remain a challenge. METHODS In a machine learning approach, we predicted individual scores for educational attainment, fluid intelligence and dimensional measures of depression, anxiety, and neuroticism using functional magnetic resonance imaging-based static and dynamic temporal synchronization between large-scale brain network nodes in 10,343 healthy individuals from the UK Biobank. In addition to using age and sex to serve as our reference point, we also predicted individual polygenic scores for related phenotypes and 13 different neuroticism traits and schizophrenia. RESULTS Beyond high accuracy for age and sex, supporting the biological sensitivity of the connectome-based features, permutation tests revealed above chance-level prediction accuracy for trait-level educational attainment and fluid intelligence. Educational attainment and fluid intelligence were mainly negatively associated with static brain connectivity in frontal and default mode networks, whereas age showed positive correlations with a more widespread pattern. In contrast, prediction accuracy was at chance level for depression, anxiety, neuroticism, and polygenic scores across traits. CONCLUSIONS These novel findings provide a benchmark for future studies linking the genetic architecture of individual and mental health traits with functional magnetic resonance imaging-based brain connectomics.
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Affiliation(s)
- Luigi A Maglanoc
- Department of Psychology, University of Oslo, Oslo, Norway; Norwegian Centre for Mental Disorders Research, Institute of Clinical Medicine, University of Oslo, & Division of Mental Health and Addiction, Oslo University Hospital, Oslo, Norway.
| | - Tobias Kaufmann
- Norwegian Centre for Mental Disorders Research, Institute of Clinical Medicine, University of Oslo, & Division of Mental Health and Addiction, Oslo University Hospital, Oslo, Norway
| | - Dennis van der Meer
- Norwegian Centre for Mental Disorders Research, Institute of Clinical Medicine, University of Oslo, & Division of Mental Health and Addiction, Oslo University Hospital, Oslo, Norway; School of Mental Health and Neuroscience, Faculty of Health, Medicine and Life Sciences, Maastricht University, Maastricht, The Netherlands
| | - Andre F Marquand
- Donders Centre for Cognitive Neuroimaging, Donders Institute for Brain, Cognition and Behaviour, Radboud University, Nijmegen, The Netherlands; Department of Neuroimaging, Institute of Psychiatry, King's College London, London, United Kingdom
| | - Thomas Wolfers
- Department of Psychology, University of Oslo, Oslo, Norway; Norwegian Centre for Mental Disorders Research, Institute of Clinical Medicine, University of Oslo, & Division of Mental Health and Addiction, Oslo University Hospital, Oslo, Norway
| | - Rune Jonassen
- Faculty of Health Sciences, Oslo Metropolitan University, Oslo, Norway
| | - Eva Hilland
- Department of Psychology, University of Oslo, Oslo, Norway; Division of Psychiatry, Diakonhjemmet Hospital, Oslo, Norway
| | - Ole A Andreassen
- Norwegian Centre for Mental Disorders Research, Institute of Clinical Medicine, University of Oslo, & Division of Mental Health and Addiction, Oslo University Hospital, Oslo, Norway
| | | | - Lars T Westlye
- Department of Psychology, University of Oslo, Oslo, Norway; Norwegian Centre for Mental Disorders Research, Institute of Clinical Medicine, University of Oslo, & Division of Mental Health and Addiction, Oslo University Hospital, Oslo, Norway.
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27
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Mallet J, Le Strat Y, Dubertret C, Gorwood P. Polygenic Risk Scores Shed Light on the Relationship between Schizophrenia and Cognitive Functioning: Review and Meta-Analysis. J Clin Med 2020; 9:E341. [PMID: 31991840 PMCID: PMC7074036 DOI: 10.3390/jcm9020341] [Citation(s) in RCA: 23] [Impact Index Per Article: 5.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/14/2019] [Revised: 01/14/2020] [Accepted: 01/23/2020] [Indexed: 12/26/2022] Open
Abstract
Schizophrenia is a multifactorial disease associated with widespread cognitive impairment. Although cognitive deficits are one of the factors most strongly associated with functional impairment in schizophrenia (SZ), current treatment strategies hardly tackle these impairments. To develop more efficient treatment strategies in patients, a better understanding of their pathogenesis is needed. Recent progress in genetics, driven by large genome-wide association studies (GWAS) and the use of polygenic risk scores (PRS), has provided new insights about the genetic architecture of complex human traits, including cognition and SZ. Here, we review the recent findings examining the genetic links between SZ and cognitive functions in population-based samples as well as in participants with SZ. The performed meta-analysis showed a negative correlation between the polygenetic risk score of schizophrenia and global cognition (p < 0.001) when the samples rely on general and healthy participants, while no significant correlation was detected when the three studies devoted to schizophrenia patients were meta-analysed (p > 0.05). Our review and meta-analysis therefore argues against universal pleiotropy for schizophrenia alleles and cognition, since cognition in SZ patients would be underpinned by the same genetic factors than in the general population, and substantially independent of common variant liability to the disorder.
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Affiliation(s)
- Jasmina Mallet
- APHP; Department of Psychiatry, Universitary Hospital Louis Mourier, 92700 Colombes, France; (Y.L.S.); (C.D.)
- Université de Paris, Institute of Psychiatry and Neuroscience of Paris (IPNP), INSERM U1266, F-75014 Paris, France
| | - Yann Le Strat
- APHP; Department of Psychiatry, Universitary Hospital Louis Mourier, 92700 Colombes, France; (Y.L.S.); (C.D.)
- Université de Paris, Institute of Psychiatry and Neuroscience of Paris (IPNP), INSERM U1266, F-75014 Paris, France
| | - Caroline Dubertret
- APHP; Department of Psychiatry, Universitary Hospital Louis Mourier, 92700 Colombes, France; (Y.L.S.); (C.D.)
- Université de Paris, Institute of Psychiatry and Neuroscience of Paris (IPNP), INSERM U1266, F-75014 Paris, France
| | - Philip Gorwood
- Université de Paris, Institute of Psychiatry and Neuroscience of Paris (IPNP), INSERM U1266, F-75014 Paris, France
- GHU Paris Psychiatrie et Neurosciences, Hôpital Sainte Anne, F-75014 Paris, France
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28
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Pisanu C, Squassina A. Treatment-Resistant Schizophrenia: Insights From Genetic Studies and Machine Learning Approaches. Front Pharmacol 2019; 10:617. [PMID: 31191325 PMCID: PMC6548883 DOI: 10.3389/fphar.2019.00617] [Citation(s) in RCA: 16] [Impact Index Per Article: 3.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/15/2019] [Accepted: 05/15/2019] [Indexed: 01/07/2023] Open
Abstract
Schizophrenia (SCZ) is a severe psychiatric disorder affecting approximately 23 million people worldwide. It is considered the eighth leading cause of disability according to the World Health Organization and is associated with a significant reduction in life expectancy. Antipsychotics represent the first-choice treatment in SCZ, but approximately 30% of patients fail to respond to acute treatment. These patients are generally defined as treatment-resistant and are eligible for clozapine treatment. Treatment-resistant patients show a more severe course of the disease, but it has been suggested that treatment-resistant schizophrenia (TRS) may constitute a distinct phenotype that is more than just a more severe form of SCZ. TRS is heritable, and genetics has been shown to play an important role in modulating response to antipsychotics. Important efforts have been put into place in order to better understand the genetic architecture of TRS, with the main goal of identifying reliable predictive markers that might improve the management and quality of life of TRS patients. However, the number of candidate gene and genome-wide association studies specifically focused on TRS is limited, and to date, findings do not allow the disentanglement of its polygenic nature. More recent studies implemented polygenic risk score, gene-based and machine learning methods to explore the genetics of TRS, reporting promising findings. In this review, we present an overview on the genetics of TRS, particularly focusing our discussion on studies implementing polygenic approaches.
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Affiliation(s)
- Claudia Pisanu
- Department of Biomedical Sciences, Section of Neuroscience and Clinical Pharmacology, University of Cagliari, Cagliari, Italy.,Department of Neuroscience, Unit of Functional Pharmacology, Uppsala University, Uppsala, Sweden
| | - Alessio Squassina
- Department of Biomedical Sciences, Section of Neuroscience and Clinical Pharmacology, University of Cagliari, Cagliari, Italy.,Department of Psychiatry, Dalhousie University, Halifax, NS, Canada
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Chen X, Zhang Q, Wang J, Xin Z, Chen J, Luo W. Combined machine learning and functional magnetic resonance imaging allows individualized prediction of high-altitude induced psychomotor impairment: The role of neural functionality in putamen and pallidum. Biosci Trends 2019; 13:98-104. [PMID: 30814403 DOI: 10.5582/bst.2019.01002] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/05/2022]
Abstract
Hypoxia exposure during high-altitude expedition cause psychomotor impairment. Neuroimaging studies indicated that the impairment may be significantly associated with neuron loss and decreased regional homogeneity (ReHo) in several brain regions, suggesting the neural functionality in these regions may be utilized to predict psychomotor impairment under exposure. In this study, 69 subjects come from Shaanxi-Tibet immigrant cohort. Reaction time (RT) tasks were performed to measure the subject's psychomotor function before and after 2-year high-altitude exposure. For each individual, the RT differences between pre-exposure and post-exposure were calculated, which were referred to as "targets" in model establishment. Rs-fMRI data were acquired at the same time with RT tasks. For each individual, the map of ReHo alteration was generated, from which the patterns would be recognized. A pattern recognition procedure was utilized to train and test the predictive models. Two different cross-validation strategies were utilized to evaluate the model performance: leave-one-out cross-validation and four-fold cross-validation. For the models displaying significant R2 and MSE, weight maps were built. As a result, the predictive models were able to decode the changes of simple and recognition reaction time from the alterations of brain activation under the exposure. The regions with highest contributions to the predictions were bilateral putamen and bilateral pallidum, suggesting that predictions were mainly based on the patterns concentrated in these regions. This study was a proof of concept study designed to examine whether individual-level psychomotor impairment under high-altitude exposure could be predicted by a combination of pattern recognition approach and neuroimaging data.
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Affiliation(s)
- Xiaoming Chen
- Department of Occupational and Environmental Health, the Ministry of Education Key Lab of Hazard Assessment and Control in Special Operational Environment, School of Public Health, Air Force Medical University
| | - Qian Zhang
- Department of Occupational and Environmental Health, the Ministry of Education Key Lab of Hazard Assessment and Control in Special Operational Environment, School of Public Health, Air Force Medical University
| | - Jiye Wang
- Department of Occupational and Environmental Health, the Ministry of Education Key Lab of Hazard Assessment and Control in Special Operational Environment, School of Public Health, Air Force Medical University
| | - Zhenlong Xin
- Department of Occupational and Environmental Health, the Ministry of Education Key Lab of Hazard Assessment and Control in Special Operational Environment, School of Public Health, Air Force Medical University
| | - Jingyuan Chen
- Department of Occupational and Environmental Health, the Ministry of Education Key Lab of Hazard Assessment and Control in Special Operational Environment, School of Public Health, Air Force Medical University
| | - Wenjing Luo
- Department of Occupational and Environmental Health, the Ministry of Education Key Lab of Hazard Assessment and Control in Special Operational Environment, School of Public Health, Air Force Medical University
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Development of Neuroimaging-Based Biomarkers in Psychiatry. ADVANCES IN EXPERIMENTAL MEDICINE AND BIOLOGY 2019; 1192:159-195. [PMID: 31705495 DOI: 10.1007/978-981-32-9721-0_9] [Citation(s) in RCA: 12] [Impact Index Per Article: 2.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/12/2022]
Abstract
This chapter presents an overview of accumulating neuroimaging data with emphasis on translational potential. The subject will be described in the context of three disease states, i.e., schizophrenia, bipolar disorder, and major depressive disorder, and for three clinical goals, i.e., disease risk assessment, subtyping, and treatment decision.
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