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Chopra S, Levi PT, Holmes A, Orchard ER, Segal A, Francey SM, O'Donoghue B, Cropley VL, Nelson B, Graham J, Baldwin L, Yuen HP, Allott K, Alvarez-Jimenez M, Harrigan S, Pantelis C, Wood SJ, McGorry P, Fornito A. Brainwide Anatomical Connectivity and Prediction of Longitudinal Outcomes in Antipsychotic-Naïve First-Episode Psychosis. Biol Psychiatry 2025; 97:157-166. [PMID: 39069164 DOI: 10.1016/j.biopsych.2024.07.016] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/17/2024] [Revised: 06/05/2024] [Accepted: 07/16/2024] [Indexed: 07/30/2024]
Abstract
BACKGROUND Disruptions of axonal connectivity are thought to be a core pathophysiological feature of psychotic illness, but whether they are present early in the illness, prior to antipsychotic exposure, and whether they can predict clinical outcome remain unknown. METHODS We acquired diffusion-weighted magnetic resonance images to map structural connectivity between each pair of 319 parcellated brain regions in 61 antipsychotic-naïve individuals with first-episode psychosis (15-25 years, 46% female) and a demographically matched sample of 27 control participants. Clinical follow-up data were also acquired in patients 3 and 12 months after the scan. We used connectome-wide analyses to map disruptions of inter-regional pairwise connectivity and connectome-based predictive modeling to predict longitudinal change in symptoms and functioning. RESULTS Individuals with first-episode psychosis showed disrupted connectivity in a brainwide network linking all brain regions compared with controls (familywise error-corrected p = .03). Baseline structural connectivity significantly predicted change in functioning over 12 months (r = 0.44, familywise error-corrected p = .041), such that lower connectivity within fronto-striato-thalamic systems predicted worse functional outcomes. CONCLUSIONS Brainwide reductions of structural connectivity exist during the early stages of psychotic illness and cannot be attributed to antipsychotic medication. Moreover, baseline measures of structural connectivity can predict change in patient functional outcomes up to 1 year after engagement with treatment services.
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Affiliation(s)
- Sidhant Chopra
- Turner Institute for Brain and Mental Health, School of Psychological Science, Monash University, Clayton, Australia; Monash Biomedical Imaging, Monash University, Clayton, Australia; Department of Psychology, Yale University, New Haven, Connecticut; Orygen, Parkville, Victoria, Australia; Centre for Youth Mental Health, The University of Melbourne, Melbourne, Victoria, Australia.
| | - Priscila T Levi
- Turner Institute for Brain and Mental Health, School of Psychological Science, Monash University, Clayton, Australia; Monash Biomedical Imaging, Monash University, Clayton, Australia
| | - Alexander Holmes
- Turner Institute for Brain and Mental Health, School of Psychological Science, Monash University, Clayton, Australia; Monash Biomedical Imaging, Monash University, Clayton, Australia
| | - Edwina R Orchard
- Yale Child Study Centre, Yale University, New Haven, Connecticut
| | - Ashlea Segal
- Turner Institute for Brain and Mental Health, School of Psychological Science, Monash University, Clayton, Australia; Monash Biomedical Imaging, Monash University, Clayton, Australia; Wu Tsai Institute, Department of Neuroscience, Yale University, New Haven, Connecticut; Department of Neuroscience, Yale School of Medicine, Yale University, New Haven, Connecticut
| | - Shona M Francey
- Orygen, Parkville, Victoria, Australia; Centre for Youth Mental Health, The University of Melbourne, Melbourne, Victoria, Australia
| | - Brian O'Donoghue
- Centre for Youth Mental Health, The University of Melbourne, Melbourne, Victoria, Australia; St. Vincent's University Hospital, Dublin 4, Ireland; Department of Psychiatry, University College Dublin, Dublin 4, Ireland
| | - Vanessa L Cropley
- Melbourne Neuropsychiatry Centre, Department of Psychiatry, University of Melbourne, Melbourne, Victoria, Australia
| | - Barnaby Nelson
- Orygen, Parkville, Victoria, Australia; Centre for Youth Mental Health, The University of Melbourne, Melbourne, Victoria, Australia
| | - Jessica Graham
- Orygen, Parkville, Victoria, Australia; Centre for Youth Mental Health, The University of Melbourne, Melbourne, Victoria, Australia
| | - Lara Baldwin
- Orygen, Parkville, Victoria, Australia; Centre for Youth Mental Health, The University of Melbourne, Melbourne, Victoria, Australia
| | - Hok Pan Yuen
- Orygen, Parkville, Victoria, Australia; Centre for Youth Mental Health, The University of Melbourne, Melbourne, Victoria, Australia
| | - Kelly Allott
- Orygen, Parkville, Victoria, Australia; Centre for Youth Mental Health, The University of Melbourne, Melbourne, Victoria, Australia
| | - Mario Alvarez-Jimenez
- Orygen, Parkville, Victoria, Australia; Centre for Youth Mental Health, The University of Melbourne, Melbourne, Victoria, Australia
| | - Susy Harrigan
- Orygen, Parkville, Victoria, Australia; Centre for Youth Mental Health, The University of Melbourne, Melbourne, Victoria, Australia; Centre for Mental Health, Melbourne School of Global and Population Health, The University of Melbourne, Parkville, Victoria, Australia
| | - Christos Pantelis
- Melbourne Neuropsychiatry Centre, Department of Psychiatry, University of Melbourne, Melbourne, Victoria, Australia; Western Hospital Sunshine, St. Albans, Victoria, Australia
| | - Stephen J Wood
- Orygen, Parkville, Victoria, Australia; Centre for Youth Mental Health, The University of Melbourne, Melbourne, Victoria, Australia; School of Psychology, University of Birmingham, Edgbaston, United Kingdom
| | - Patrick McGorry
- Orygen, Parkville, Victoria, Australia; Centre for Youth Mental Health, The University of Melbourne, Melbourne, Victoria, Australia
| | - Alex Fornito
- Turner Institute for Brain and Mental Health, School of Psychological Science, Monash University, Clayton, Australia; Monash Biomedical Imaging, Monash University, Clayton, Australia
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2
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Dauvermann MR, Costello L, Tronchin G, Corley E, Holleran L, Mothersill D, Rokita KI, Kane R, Hallahan B, McDonald C, Pasternak O, Donohoe G, Cannon DM. Cellular and extracellular white matter alterations after childhood trauma experience in individuals with schizophrenia. Psychol Med 2025:1-10. [PMID: 39757719 DOI: 10.1017/s0033291724003064] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 01/07/2025]
Abstract
BACKGROUND Childhood trauma (CT) is related to altered fractional anisotropy (FA) in individuals with schizophrenia (SZ). However, it remains unclear whether CT may influence specific cellular or extracellular compartments of FA in SZ with CT experience. We extended our previous study on FA in SZ (Costello et al., 2023) and examined the impact of CT on hypothesized lower free water-corrected FA (FAT) and higher extracellular free water (FW). METHOD Thirty-seven SZ and 129 healthy controls (HC) were grouped into the 'none/low' or 'high' CT group. All participants underwent diffusion-weighted magnetic resonance imaging. We performed tract-based spatial statistics to study the main effects of diagnostic group and CT, and the interaction between CT and diagnostic group across FAT and FW. RESULTS SZ displayed lower FAT within the corpus callosum and corona radiata compared to HC (p < 0.05, Threshold-Free Cluster Enhancement (TFCE)). Independent of diagnosis, we observed lower FAT (p < 0.05, TFCE) and higher FW (p < 0.05, TFCE) in both SZ and HC with high CT levels compared to SZ and HC with none or low CT levels. Furthermore, we did not identify an interaction between CT and diagnostic group (p > 0.05, TFCE). CONCLUSIONS These novel findings suggest that the impact of CT on lower FAT may reflect cellular rather than extracellular alterations in established schizophrenia. This highlights the impact of CT on white matter microstructure, regardless of diagnostic status.
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Affiliation(s)
- Maria R Dauvermann
- Center for Neuroimaging, Cognition and Genomics (NICOG), Clinical Neuroimaging Laboratory, Galway Neuroscience Centre, University of Galway, Galway, Ireland
- Institute for Mental Health, School of Psychology, University of Birmingham, Birmingham, UK
| | - Laura Costello
- Center for Neuroimaging, Cognition and Genomics (NICOG), Clinical Neuroimaging Laboratory, Galway Neuroscience Centre, University of Galway, Galway, Ireland
| | - Giulia Tronchin
- Center for Neuroimaging, Cognition and Genomics (NICOG), Clinical Neuroimaging Laboratory, Galway Neuroscience Centre, University of Galway, Galway, Ireland
| | - Emma Corley
- Center for Neuroimaging, Cognition and Genomics (NICOG), Clinical Neuroimaging Laboratory, Galway Neuroscience Centre, University of Galway, Galway, Ireland
| | - Laurena Holleran
- Center for Neuroimaging, Cognition and Genomics (NICOG), Clinical Neuroimaging Laboratory, Galway Neuroscience Centre, University of Galway, Galway, Ireland
| | - David Mothersill
- Center for Neuroimaging, Cognition and Genomics (NICOG), Clinical Neuroimaging Laboratory, Galway Neuroscience Centre, University of Galway, Galway, Ireland
- Department of Psychology, School of Business, National College of Ireland, Dublin, Ireland
- Department of Psychiatry, Trinity College Dublin, St. James's Hospital, Dublin, Ireland
| | - Karolina I Rokita
- Center for Neuroimaging, Cognition and Genomics (NICOG), Clinical Neuroimaging Laboratory, Galway Neuroscience Centre, University of Galway, Galway, Ireland
| | - Ruán Kane
- Center for Neuroimaging, Cognition and Genomics (NICOG), Clinical Neuroimaging Laboratory, Galway Neuroscience Centre, University of Galway, Galway, Ireland
| | - Brian Hallahan
- Center for Neuroimaging, Cognition and Genomics (NICOG), Clinical Neuroimaging Laboratory, Galway Neuroscience Centre, University of Galway, Galway, Ireland
| | - Colm McDonald
- Center for Neuroimaging, Cognition and Genomics (NICOG), Clinical Neuroimaging Laboratory, Galway Neuroscience Centre, University of Galway, Galway, Ireland
| | - Ofer Pasternak
- Department of Radiology, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
- Department of Psychiatry, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
- Department of Psychiatry, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
| | - Gary Donohoe
- Center for Neuroimaging, Cognition and Genomics (NICOG), Clinical Neuroimaging Laboratory, Galway Neuroscience Centre, University of Galway, Galway, Ireland
| | - Dara M Cannon
- Center for Neuroimaging, Cognition and Genomics (NICOG), Clinical Neuroimaging Laboratory, Galway Neuroscience Centre, University of Galway, Galway, Ireland
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Li J, He J, Ren H, Li Z, Ma X, Yuan L, Ouyang L, Liao A, Peng H, He Y, Tang J, Chen X. Free-water imaging in subcortical gray matter in schizophrenia patients with persistent auditory verbal hallucinations. Schizophr Res 2024; 274:517-525. [PMID: 39566119 DOI: 10.1016/j.schres.2024.10.028] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/15/2023] [Revised: 09/25/2024] [Accepted: 10/29/2024] [Indexed: 11/22/2024]
Abstract
Subcortical gray matter (SGM) is increasingly linked to the pathophysiology of schizophrenia, specifically auditory verbal hallucinations (AVHs). However, few studies have been conducted on the role of extracellular free-water (FW) and white matter microstructural abnormalities in AVHs within the SGM using the free water elimination technique. We conducted a comprehensive investigation of macroscopic volume, FW, and white matter microstructure in the SGM of 60 schizophrenia patients with persistent AVHs (p-AVH), 36 patients no AVH history (n-AVH), and 43 healthy control participants (HC). No macroscopic volume abnormalities were found in the p-AVH or n-AVH groups. However, abnormalities in both FW and microstructures were detected in multiple SGM structures of individuals with schizophrenia. Importantly, unlike the n-AVH group, the p-AVH group exhibited FW and microstructure abnormalities in the bilateral caudate that correlated with AVH severity. Our findings suggest that regardless of the presence of AVHs, FW and microstructure abnormalities in the SGM are more pronounced than macroscopic volume abnormalities in patients with schizophrenia. The bilateral caudate may be a key factor in the mechanisms underlying AVHs. Additionally, normal FW and microstructure in the bilateral caudate may be a notable characteristic of n-AVH patients.
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Affiliation(s)
- Jinguang Li
- Department of Psychiatry, National Clinical Research Center for Mental Disorders, National Center for Mental Disorders, The Second Xiangya Hospital of Central South University, Changsha, Hunan, China; Department of Psychiatry, Wuhan Mental Health Center, Wuhan, China
| | - Jingqi He
- Department of Psychiatry, Sir Run-Run Shaw Hospital, School of Medicine, Zhejiang University, Hangzhou, China
| | - Honghong Ren
- Department of Psychiatry, National Clinical Research Center for Mental Disorders, National Center for Mental Disorders, The Second Xiangya Hospital of Central South University, Changsha, Hunan, China; Shandong Provincial Hospital Affiliated to Shandong First Medical University, Shandong, China
| | - Zongchang Li
- Department of Psychiatry, National Clinical Research Center for Mental Disorders, National Center for Mental Disorders, The Second Xiangya Hospital of Central South University, Changsha, Hunan, China
| | - Xiaoqian Ma
- Department of Psychiatry, National Clinical Research Center for Mental Disorders, National Center for Mental Disorders, The Second Xiangya Hospital of Central South University, Changsha, Hunan, China
| | - Liu Yuan
- Department of Psychiatry, National Clinical Research Center for Mental Disorders, National Center for Mental Disorders, The Second Xiangya Hospital of Central South University, Changsha, Hunan, China
| | - Lijun Ouyang
- Department of Psychiatry, National Clinical Research Center for Mental Disorders, National Center for Mental Disorders, The Second Xiangya Hospital of Central South University, Changsha, Hunan, China
| | - Aijun Liao
- Department of Psychiatry, National Clinical Research Center for Mental Disorders, National Center for Mental Disorders, The Second Xiangya Hospital of Central South University, Changsha, Hunan, China
| | - Huiqing Peng
- Department of Psychiatry, National Clinical Research Center for Mental Disorders, National Center for Mental Disorders, The Second Xiangya Hospital of Central South University, Changsha, Hunan, China
| | - Ying He
- Department of Psychiatry, National Clinical Research Center for Mental Disorders, National Center for Mental Disorders, The Second Xiangya Hospital of Central South University, Changsha, Hunan, China.
| | - Jinsong Tang
- Department of Psychiatry, Sir Run-Run Shaw Hospital, School of Medicine, Zhejiang University, Hangzhou, China.
| | - Xiaogang Chen
- Department of Psychiatry, National Clinical Research Center for Mental Disorders, National Center for Mental Disorders, The Second Xiangya Hospital of Central South University, Changsha, Hunan, China.
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4
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Cho KIK, Zhang F, Penzel N, Seitz-Holland J, Tang Y, Zhang T, Xu L, Li H, Keshavan M, Whitfield-Gabrieli S, Niznikiewicz M, Stone WS, Wang J, Shenton ME, Pasternak O. Excessive interstitial free-water in cortical gray matter preceding accelerated volume changes in individuals at clinical high risk for psychosis. Mol Psychiatry 2024; 29:3623-3634. [PMID: 38830974 DOI: 10.1038/s41380-024-02597-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/17/2023] [Revised: 05/01/2024] [Accepted: 05/03/2024] [Indexed: 06/05/2024]
Abstract
Recent studies show that accelerated cortical gray matter (GM) volume reduction seen in anatomical MRI can help distinguish between individuals at clinical high risk (CHR) for psychosis who will develop psychosis and those who will not. This reduction is suggested to represent atypical developmental or degenerative changes accompanying an accumulation of microstructural changes, such as decreased spine density and dendritic arborization. Detecting the microstructural sources of these changes before they accumulate into volume loss is crucial. Our study aimed to detect these microstructural GM alterations using diffusion MRI (dMRI). We tested for baseline and longitudinal group differences in anatomical and dMRI data from 160 individuals at CHR and 96 healthy controls (HC) acquired in a single imaging site. Of the CHR individuals, 33 developed psychosis (CHR-P), while 127 did not (CHR-NP). Among all participants, longitudinal data was available for 45 HCs, 17 CHR-P, and 66 CHR-NP. Eight cortical lobes were examined for GM volume and GM microstructure. A novel dMRI measure, interstitial free water (iFW), was used to quantify GM microstructure by eliminating cerebrospinal fluid contribution. Additionally, we assessed whether these measures differentiated the CHR-P from the CHR-NP. In addition, for completeness, we also investigated changes in cortical thickness and in white matter (WM) microstructure. At baseline the CHR group had significantly higher iFW than HC in the prefrontal, temporal, parietal, and occipital lobes, while volume was reduced only in the temporal lobe. Neither iFW nor volume differentiated between the CHR-P and CHR-NP groups at baseline. However, in many brain areas, the CHR-P group demonstrated significantly accelerated changes (iFW increase and volume reduction) with time than the CHR-NP group. Cortical thickness provided similar results as volume, and there were no significant changes in WM microstructure. Our results demonstrate that microstructural GM changes in individuals at CHR have a wider extent than volumetric changes or microstructural WM changes, and they predate the acceleration of brain changes that occur around psychosis onset. Microstructural GM changes, as reflected by the increased iFW, are thus an early pathology at the prodromal stage of psychosis that may be useful for a better mechanistic understanding of psychosis development.
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Affiliation(s)
- Kang Ik K Cho
- Department of Psychiatry, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
| | - Fan Zhang
- Department of Radiology, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
| | - Nora Penzel
- Department of Psychiatry, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
| | - Johanna Seitz-Holland
- Department of Psychiatry, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
- Department of Psychiatry, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
| | - Yingying Tang
- Shanghai Key Laboratory of Psychotic Disorders, Shanghai Mental Health Center, Shanghai Jiao Tong University School of Medicine, Shanghai, China
- Brain Science and Technology Research Center, Shanghai Jiao Tong University, Shanghai, China
| | - Tianhong Zhang
- Shanghai Key Laboratory of Psychotic Disorders, Shanghai Mental Health Center, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Lihua Xu
- Shanghai Key Laboratory of Psychotic Disorders, Shanghai Mental Health Center, Shanghai Jiao Tong University School of Medicine, Shanghai, China
- Brain Science and Technology Research Center, Shanghai Jiao Tong University, Shanghai, China
| | - Huijun Li
- Department of Psychology, Florida A&M University, Tallahassee, FL, USA
| | - Matcheri Keshavan
- The Massachusetts Mental Health Center, Public Psychiatry Division, Beth Israel Deaconess Medical Center, and Harvard Medical School, Boston, MA, USA
| | - Susan Whitfield-Gabrieli
- Department of Psychology, Northeastern University, Boston, MA, USA
- The McGovern Institute for Brain Research and the Poitras Center for Affective Disorders Research, Massachusetts Institute of Technology, Cambridge, MA, USA
| | - Margaret Niznikiewicz
- The Department of Psychiatry, Veterans Affairs Boston Healthcare System, Brockton Division, Brockton, MA, USA
| | - William S Stone
- The Massachusetts Mental Health Center, Public Psychiatry Division, Beth Israel Deaconess Medical Center, and Harvard Medical School, Boston, MA, USA
| | - Jijun Wang
- Shanghai Key Laboratory of Psychotic Disorders, Shanghai Mental Health Center, Shanghai Jiao Tong University School of Medicine, Shanghai, China.
- Brain Science and Technology Research Center, Shanghai Jiao Tong University, Shanghai, China.
| | - Martha E Shenton
- Department of Psychiatry, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
- Department of Radiology, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
- Department of Psychiatry, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
| | - Ofer Pasternak
- Department of Psychiatry, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA.
- Department of Radiology, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA.
- Department of Psychiatry, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA.
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5
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Fahira A, Syed AAS, Jian X, Yang Q, Zheng C, Wadood A, Wang Z, Huang Z, Shi Y. Genome-wide association of common genetic variants and functional annotation analysis of schizophrenia and white matter abnormalities. Asian J Psychiatr 2024; 102:104236. [PMID: 39447315 DOI: 10.1016/j.ajp.2024.104236] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/12/2024] [Revised: 07/28/2024] [Accepted: 09/04/2024] [Indexed: 10/26/2024]
Affiliation(s)
- Aamir Fahira
- Key Laboratory of Big Data Mining and Precision Drug Design of Guangdong Medical University, Key Laboratory for Research and Development of Natural Drugs of Guangdong Province, School of Pharmacy, Guangdong Medical University, Dongguan, Guangdong 523808, China; Bio-X Institutes, Key Laboratory for the Genetics of Developmental and Neuropsychiatric Disorders (Ministry of Education), Shanghai Jiao Tong University, Shanghai 200030, China
| | - Ali Alamdar Shah Syed
- Bio-X Institutes, Key Laboratory for the Genetics of Developmental and Neuropsychiatric Disorders (Ministry of Education), Shanghai Jiao Tong University, Shanghai 200030, China
| | - Xuemin Jian
- Bio-X Institutes, Key Laboratory for the Genetics of Developmental and Neuropsychiatric Disorders (Ministry of Education), Shanghai Jiao Tong University, Shanghai 200030, China
| | - Qiangzhen Yang
- Bio-X Institutes, Key Laboratory for the Genetics of Developmental and Neuropsychiatric Disorders (Ministry of Education), Shanghai Jiao Tong University, Shanghai 200030, China
| | - Chenxiang Zheng
- Bio-X Institutes, Key Laboratory for the Genetics of Developmental and Neuropsychiatric Disorders (Ministry of Education), Shanghai Jiao Tong University, Shanghai 200030, China
| | - Abdul Wadood
- Department of Biochemistry, Abdul Wali Khan University Mardan, Pakistan
| | - Zhuo Wang
- Bio-X Institutes, Key Laboratory for the Genetics of Developmental and Neuropsychiatric Disorders (Ministry of Education), Shanghai Jiao Tong University, Shanghai 200030, China
| | - Zunnan Huang
- Key Laboratory of Big Data Mining and Precision Drug Design of Guangdong Medical University, Key Laboratory for Research and Development of Natural Drugs of Guangdong Province, School of Pharmacy, Guangdong Medical University, Dongguan, Guangdong 523808, China.
| | - Yongyong Shi
- Bio-X Institutes, Key Laboratory for the Genetics of Developmental and Neuropsychiatric Disorders (Ministry of Education), Shanghai Jiao Tong University, Shanghai 200030, China; Biomedical Sciences Institute of Qingdao University (Qingdao Branch of SJTU Bio-X Institutes), Qingdao University, Qingdao 266003, China; Shanghai Key Laboratory of Psychotic Disorders, Shanghai Mental Health Center, Shanghai Jiao Tong University School of Medicine, Shanghai 200030, China; Shanghai Key Laboratory of Sleep Disordered Breathing, Shanghai Jiao Tong University Affiliated Sixth People's Hospital, Shanghai, China; Department of Psychiatry, First Teaching Hospital of Xinjiang Medical University, Urumqi 830046, China.
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6
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Wannan CMJ, Eratne D, Santillo AF, Malpas C, Cilia B, Dean OM, Walker A, Berk M, Bousman C, Everall I, Velakoulis D, Pantelis C. Plasma neurofilament light protein is differentially associated with age in individuals with treatment-resistant schizophrenia and bipolar affective disorder compared to controls. Psychiatry Res 2024; 339:116073. [PMID: 39024892 DOI: 10.1016/j.psychres.2024.116073] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/04/2023] [Revised: 06/23/2024] [Accepted: 06/29/2024] [Indexed: 07/20/2024]
Abstract
Accelerated brain ageing has been observed in multiple psychiatric disorders. This study examined whether relationships between age and plasma neurofilament light (NfL) protein differed in individuals with psychiatric disorders (major depressive disorder (n = 42), bipolar affective disorder (n = 121), treatment-resistant schizophrenia (TRS, n = 82)) compared to two healthy control (HC) groups (n = 1,926 and n = 59). Compared to two independent HC samples, individuals with TRS demonstrated a stronger positive relationship between age and NfL levels. Individuals with BPAD had a stronger negative relationship between age and NfL levels compared to the large normative HC cohort, but not locally-acquired HCs. These findings show that plasma NfL levels are differentially associated with age in individuals with TRS and BPAD compared to healthy individuals.
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Affiliation(s)
- Cassandra M J Wannan
- Melbourne Neuropsychiatry Centre, Department of Psychiatry, The University of Melbourne, Melbourne, VIC, Australia; Centre for Youth Mental Health, The University of Melbourne, Parkville, Victoria, Australia; Orygen, Parkville, Victoria, Australia.
| | - Dhamidhu Eratne
- Melbourne Neuropsychiatry Centre, Department of Psychiatry, The University of Melbourne, Melbourne, VIC, Australia; Neuropsychiatry Centre, The Royal Melbourne Hospital, Melbourne, VIC, Australia
| | - Alexander F Santillo
- Department of Clinical Sciences, Clinical Memory Research Unit, Faculty of Medicine, Lund University, Lund/Malmö, Sweden
| | - Charles Malpas
- Department of Medicine, Royal Melbourne Hospital, University of Melbourne, Australia; Melbourne School of Psychological Sciences, University of Melbourne, Australia
| | - Brandon Cilia
- Neuropsychiatry Centre, The Royal Melbourne Hospital, Melbourne, VIC, Australia
| | - Olivia M Dean
- Deakin University, IMPACT - the Institute for Mental and Physical Health and Clinical Translation, School of Medicine, Barwon Health, Geelong, Australia; Florey Institute of Neuroscience and Mental Health, Parkville, Australia
| | - Adam Walker
- Deakin University, IMPACT - the Institute for Mental and Physical Health and Clinical Translation, School of Medicine, Barwon Health, Geelong, Australia
| | - Michael Berk
- Deakin University, IMPACT - the Institute for Mental and Physical Health and Clinical Translation, School of Medicine, Barwon Health, Geelong, Australia
| | - Chad Bousman
- Department of Medical Genetics, University of Calgary, Calgary, Canada; Department of Psychiatry, University of Melbourne, Melbourne, Australia
| | - Ian Everall
- Institute of Psychiatry, Psychology & Neuroscience, King's College London, London, UK
| | - Dennis Velakoulis
- Melbourne Neuropsychiatry Centre, Department of Psychiatry, The University of Melbourne, Melbourne, VIC, Australia; Neuropsychiatry Centre, The Royal Melbourne Hospital, Melbourne, VIC, Australia
| | - Christos Pantelis
- Melbourne Neuropsychiatry Centre, Department of Psychiatry, The University of Melbourne, Melbourne, VIC, Australia; Department of Psychiatry, University of Melbourne, Melbourne, Australia; Western Centre for Health Research & Education, University of Melbourne & Western Health, Sunshine Hospital, St Albans, VIC, Australia; Monash Institute of Pharmaceutical Sciences (MIPS), Monash University, Parkville, Vic, Australia
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7
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Newlin NR, Kim ME, Kanakaraj P, Yao T, Hohman T, Pechman KR, Beason-Held LL, Resnick SM, Archer D, Jefferson A, Landman BA, Moyer D. MidRISH: Unbiased harmonization of rotationally invariant harmonics of the diffusion signal. Magn Reson Imaging 2024; 111:113-119. [PMID: 38537892 PMCID: PMC11283839 DOI: 10.1016/j.mri.2024.03.033] [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: 08/09/2023] [Revised: 03/09/2024] [Accepted: 03/20/2024] [Indexed: 04/09/2024]
Abstract
Data harmonization is necessary for removing confounding effects in multi-site diffusion image analysis. One such harmonization method, LinearRISH, scales rotationally invariant spherical harmonic (RISH) features from one site ("target") to the second ("reference") to reduce confounding scanner effects. However, reference and target site designations are not arbitrary and resultant diffusion metrics (fractional anisotropy, mean diffusivity) are biased by this choice. In this work we propose MidRISH: rather than scaling reference RISH features to target RISH features, we project both sites to a mid-space. We validate MidRISH with the following experiments: harmonizing scanner differences from 37 matched patients free of cognitive impairment, and harmonizing acquisition and study differences on 117 matched patients free of cognitive impairment. We find that MidRISH reduces bias of reference selection while preserving harmonization efficacy of LinearRISH. Users should be cautious when performing LinearRISH harmonization. To select a reference site is to choose diffusion metric effect-size. Our proposed method eliminates the bias-inducing site selection step.
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Affiliation(s)
- Nancy R Newlin
- Department of Computer Science, Vanderbilt University, Nashville, TN, USA.
| | - Michael E Kim
- Department of Computer Science, Vanderbilt University, Nashville, TN, USA
| | | | - Tianyuan Yao
- Department of Computer Science, Vanderbilt University, Nashville, TN, USA
| | - Timothy Hohman
- Vanderbilt Memory and Alzheimer's Center, Vanderbilt University Medical Center, Nashville, TN, USA; Department of Neurology, Vanderbilt University Medical Center, Nashville, TN, USA; Vanderbilt Genetics Institute, Vanderbilt University School of Medicine, Nashville, TN, USA
| | - Kimberly R Pechman
- Vanderbilt Memory and Alzheimer's Center, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Lori L Beason-Held
- Laboratory of Behavioral Neuroscience, National Institute on Aging, National Institutes of Health, Baltimore, MD, USA
| | - Susan M Resnick
- Laboratory of Behavioral Neuroscience, National Institute on Aging, National Institutes of Health, Baltimore, MD, USA
| | - Derek Archer
- Vanderbilt Memory and Alzheimer's Center, Vanderbilt University Medical Center, Nashville, TN, USA; Department of Neurology, Vanderbilt University Medical Center, Nashville, TN, USA; Vanderbilt Genetics Institute, Vanderbilt University School of Medicine, Nashville, TN, USA
| | - Angela Jefferson
- Vanderbilt Memory and Alzheimer's Center, Vanderbilt University Medical Center, Nashville, TN, USA; Department of Neurology, Vanderbilt University Medical Center, Nashville, TN, USA; Department of Medicine, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Bennett A Landman
- Department of Computer Science, Vanderbilt University, Nashville, TN, USA; Vanderbilt University Institute of Imaging Science, Vanderbilt University, Nashville, TN, USA
| | - Daniel Moyer
- Department of Computer Science, Vanderbilt University, Nashville, TN, USA
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8
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Segal A, Smith RE, Chopra S, Oldham S, Parkes L, Aquino K, Kia SM, Wolfers T, Franke B, Hoogman M, Beckmann CF, Westlye LT, Andreassen OA, Zalesky A, Harrison BJ, Davey CG, Soriano-Mas C, Cardoner N, Tiego J, Yücel M, Braganza L, Suo C, Berk M, Cotton S, Bellgrove MA, Marquand AF, Fornito A. Multiscale heterogeneity of white matter morphometry in psychiatric disorders. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.08.04.606523. [PMID: 39149253 PMCID: PMC11326206 DOI: 10.1101/2024.08.04.606523] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Subscribe] [Scholar Register] [Indexed: 08/17/2024]
Abstract
Background Inter-individual variability in neurobiological and clinical characteristics in mental illness is often overlooked by classical group-mean case-control studies. Studies using normative modelling to infer person-specific deviations of grey matter volume have indicated that group means are not representative of most individuals. The extent to which this variability is present in white matter morphometry, which is integral to brain function, remains unclear. Methods We applied Warped Bayesian Linear Regression normative models to T1-weighted magnetic resonance imaging data and mapped inter-individual variability in person-specific white matter volume deviations in 1,294 cases (58% male) diagnosed with one of six disorders (attention-deficit/hyperactivity, autism, bipolar, major depressive, obsessive-compulsive and schizophrenia) and 1,465 matched controls (54% male) recruited across 25 scan sites. We developed a framework to characterize deviation heterogeneity at multiple spatial scales, from individual voxels, through inter-regional connections, specific brain regions, and spatially extended brain networks. Results The specific locations of white matter volume deviations were highly heterogeneous across participants, affecting the same voxel in fewer than 8% of individuals with the same diagnosis. For autism and schizophrenia, negative deviations (i.e., areas where volume is lower than normative expectations) aggregated into common tracts, regions and large-scale networks in up to 35% of individuals. Conclusions The prevalence of white matter volume deviations was lower than previously observed in grey matter, and the specific location of these deviations was highly heterogeneous when considering voxel-wise spatial resolution. Evidence of aggregation within common pathways and networks was apparent in schizophrenia and autism but not other disorders.
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Affiliation(s)
- Ashlea Segal
- Turner Institute for Brain and Mental Health, School of Psychological Sciences, Monash University, Melbourne, Australia
- Monash Biomedical Imaging, Monash University, Melbourne, Australia
- Wu Tsai Institute, Department of Neuroscience, Yale University, New Haven, United States
| | - Robert E Smith
- The Florey Institute of Neuroscience and Mental Health, Heidelberg, Victoria, Australia
- Florey Department of Neuroscience and Mental Health, Parkville, Victoria, Australia
| | - Sidhant Chopra
- Department of Psychology, Yale University, New Haven, CT, USA
| | - Stuart Oldham
- Turner Institute for Brain and Mental Health, School of Psychological Sciences, Monash University, Melbourne, Australia
- Monash Biomedical Imaging, Monash University, Melbourne, Australia
- Developmental Imaging, Murdoch Children’s Research Institute, Melbourne, Australia
| | - Linden Parkes
- Department of Psychiatry, Rutgers University, Piscataway, NJ 08854, USA
| | | | - Seyed Mostafa Kia
- Donders Centre for Cognitive Neuroimaging, Radboud University, Nijmegen, the Netherlands
- Donders Institute for Brain, Cognition and Behaviour, Radboud University, Nijmegen, the Netherlands
- Department of Psychiatry, University Medical Center Utrecht, Utrecht, The Netherlands
- Department of Cognitive Science and Artificial Intelligence, Tilburg School of Humanities and Digital Sciences, Tilburg University, Tilburg, the Netherlands
| | - Thomas Wolfers
- Donders Institute for Brain, Cognition and Behaviour, Radboud University, Nijmegen, the Netherlands
- Norwegian Centre for Mental Disorders Research (NORMENT), Division of Mental Health and Addiction, University of Oslo & Oslo University Hospital, Oslo, Norway
- Department of Psychiatry and Psychotherapy, Tübingen Center for Mental Health (TÜCMH), University of Tübingen, Tübingen, Germany
| | - Barbara Franke
- Department of Cognitive Neuroscience, Donders Institute of Brain, Cognition and Behaviour, Radboud University Medical Center, Nijmegen, The Netherlands
- Department of Human Genetics, Donders Institute of Brain, Cognition and Behaviour, Radboud University Medical Center, Nijmegen, The Netherlands
| | - Martine Hoogman
- Department of Human Genetics, Donders Institute of Brain, Cognition and Behaviour, Radboud University Medical Center, Nijmegen, The Netherlands
- Department of Psychiatry, Donders Institute of Brain, Cognition and Behaviour, Radboud University Medical Center, Nijmegen, The Netherlands
| | - Christian F. Beckmann
- Donders Institute for Brain, Cognition and Behaviour, Radboud University, Nijmegen, the Netherlands
- Department of Cognitive Neuroscience, Radboud University Medical Centre, Nijmegen, The Netherlands
- Centre for Functional MRI of the Brain (FMRIB), Nuffield Department of Clinical Neurosciences, Wellcome Centre for Integrative Neuroimaging, University of Oxford, Oxford, United Kingdom
| | - Lars T. Westlye
- Norwegian Centre for Mental Disorders Research (NORMENT), Division of Mental Health and Addiction, University of Oslo & Oslo University Hospital, Oslo, Norway
- Department of Psychology, University of Oslo, Oslo, Norway
- KG Jebsen Centre for Neurodevelopmental Disorders, University of Oslo and Oslo University Hospital, Oslo, Norway
| | - Ole A. Andreassen
- Department of Psychology, University of Oslo, Oslo, Norway
- KG Jebsen Centre for Neurodevelopmental Disorders, University of Oslo and Oslo University Hospital, Oslo, Norway
| | - Andrew Zalesky
- Melbourne Neuropsychiatry Centre, Department of Psychiatry, The University of Melbourne, Victoria, Australia
- Department of Biomedical Engineering, The University of Melbourne, Victoria, Australia
| | - Ben J. Harrison
- Department of Psychiatry, The University of Melbourne, Victoria, Australia
| | | | - Carles Soriano-Mas
- Department of Psychiatry, Bellvitge University Hospital. Bellvitge Biomedical Research Institute (IDIBELL), Barcelona, Spain
- Centro de Investigación Biomédica en Red de Salud Mental (CIBERSAM), Carlos III Health Institute, Madrid, Spain
- Department of Social Psychology and Quantitative Psychology, Universitat de Barcelona-UB, Barcelona, Spain
| | - Narcís Cardoner
- Centro de Investigación Biomédica en Red de Salud Mental (CIBERSAM), Carlos III Health Institute, Madrid, Spain
- Sant Pau Mental Health Research Group, Institut d’Investigació Biomèdica Sant Pau (IIB-Sant Pau), Hospital de la Santa Creu i Sant Pau, Barcelona, Spain
- Department of Psychiatry and Forensic Medicine, Universitat Autònoma de Barcelona, Barcelona, Spain
| | - Jeggan Tiego
- Turner Institute for Brain and Mental Health, School of Psychological Sciences, Monash University, Melbourne, Australia
- Monash Biomedical Imaging, Monash University, Melbourne, Australia
| | - Murat Yücel
- Turner Institute for Brain and Mental Health, School of Psychological Sciences, Monash University, Melbourne, Australia
- QIMR Berghofer Medical Research Institute, Brisbane, Queensland, Australia
| | - Leah Braganza
- Turner Institute for Brain and Mental Health, School of Psychological Sciences, Monash University, Melbourne, Australia
| | - Chao Suo
- Turner Institute for Brain and Mental Health, School of Psychological Sciences, Monash University, Melbourne, Australia
- Monash Biomedical Imaging, Monash University, Melbourne, Australia
- Australian Characterisation Commons at Scale (ACCS) Project, Monash eResearch Centre, Melbourne, Australia
| | - Michael Berk
- Deakin University, IMPACT – the Institute for Mental and Physical Health and Clinical Translation, School of Medicine, Barwon Health, Geelong, Australia
- Orygen, Melbourne, Australia
- Centre for Youth Mental Health, University of Melbourne, Melbourne, Australia
- Florey Institute for Neuroscience and Mental Health, Parkville, Australia
| | - Sue Cotton
- Orygen, Melbourne, Australia
- Centre for Youth Mental Health, University of Melbourne, Melbourne, Australia
| | - Mark A. Bellgrove
- Turner Institute for Brain and Mental Health, School of Psychological Sciences, Monash University, Melbourne, Australia
| | - Andre F. Marquand
- Donders Centre for Cognitive Neuroimaging, Radboud University, Nijmegen, the Netherlands
- Donders Institute for Brain, Cognition and Behaviour, Radboud University, Nijmegen, the Netherlands
- Department of Cognitive Neuroscience, Radboud University Medical Centre, Nijmegen, The Netherlands
- Department of Neuroimaging, Centre of Neuroimaging Sciences, Institute of Psychiatry, King’s College London, London, The United Kingdom
| | - Alex Fornito
- Turner Institute for Brain and Mental Health, School of Psychological Sciences, Monash University, Melbourne, Australia
- Monash Biomedical Imaging, Monash University, Melbourne, Australia
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9
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Gao C, Bao S, Kim ME, Newlin NR, Kanakaraj P, Yao T, Rudravaram G, Huo Y, Moyer D, Schilling K, Kukull WA, Toga AW, Archer DB, Hohman TJ, Landman BA, Li Z. Field-of-view extension for brain diffusion MRI via deep generative models. J Med Imaging (Bellingham) 2024; 11:044008. [PMID: 39185475 PMCID: PMC11344266 DOI: 10.1117/1.jmi.11.4.044008] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/25/2024] [Revised: 08/01/2024] [Accepted: 08/01/2024] [Indexed: 08/27/2024] Open
Abstract
Purpose In brain diffusion magnetic resonance imaging (dMRI), the volumetric and bundle analyses of whole-brain tissue microstructure and connectivity can be severely impeded by an incomplete field of view (FOV). We aim to develop a method for imputing the missing slices directly from existing dMRI scans with an incomplete FOV. We hypothesize that the imputed image with a complete FOV can improve whole-brain tractography for corrupted data with an incomplete FOV. Therefore, our approach provides a desirable alternative to discarding the valuable brain dMRI data, enabling subsequent tractography analyses that would otherwise be challenging or unattainable with corrupted data. Approach We propose a framework based on a deep generative model that estimates the absent brain regions in dMRI scans with an incomplete FOV. The model is capable of learning both the diffusion characteristics in diffusion-weighted images (DWIs) and the anatomical features evident in the corresponding structural images for efficiently imputing missing slices of DWIs in the incomplete part of the FOV. Results For evaluating the imputed slices, on the Wisconsin Registry for Alzheimer's Prevention (WRAP) dataset, the proposed framework achievedPSNR b 0 = 22.397 ,SSIM b 0 = 0.905 ,PSNR b 1300 = 22.479 , andSSIM b 1300 = 0.893 ; on the National Alzheimer's Coordinating Center (NACC) dataset, it achievedPSNR b 0 = 21.304 ,SSIM b 0 = 0.892 ,PSNR b 1300 = 21.599 , andSSIM b 1300 = 0.877 . The proposed framework improved the tractography accuracy, as demonstrated by an increased average Dice score for 72 tracts ( p < 0.001 ) on both the WRAP and NACC datasets. Conclusions Results suggest that the proposed framework achieved sufficient imputation performance in brain dMRI data with an incomplete FOV for improving whole-brain tractography, thereby repairing the corrupted data. Our approach achieved more accurate whole-brain tractography results with an extended and complete FOV and reduced the uncertainty when analyzing bundles associated with Alzheimer's disease.
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Affiliation(s)
- Chenyu Gao
- Vanderbilt University, Department of Electrical and Computer Engineering, Nashville, Tennessee, United States
| | - Shunxing Bao
- Vanderbilt University, Department of Electrical and Computer Engineering, Nashville, Tennessee, United States
| | - Michael E. Kim
- Vanderbilt University, Department of Computer Science, Nashville, Tennessee, United States
| | - Nancy R. Newlin
- Vanderbilt University, Department of Computer Science, Nashville, Tennessee, United States
| | - Praitayini Kanakaraj
- Vanderbilt University, Department of Computer Science, Nashville, Tennessee, United States
| | - Tianyuan Yao
- Vanderbilt University, Department of Computer Science, Nashville, Tennessee, United States
| | - Gaurav Rudravaram
- Vanderbilt University, Department of Electrical and Computer Engineering, Nashville, Tennessee, United States
| | - Yuankai Huo
- Vanderbilt University, Department of Computer Science, Nashville, Tennessee, United States
| | - Daniel Moyer
- Vanderbilt University, Department of Computer Science, Nashville, Tennessee, United States
| | - Kurt Schilling
- Vanderbilt University Medical Center, Department of Radiology and Radiological Sciences, Nashville, Tennessee, United States
| | - Walter A. Kukull
- University of Washington, Department of Epidemiology, Seattle, Washington, United States
| | - Arthur W. Toga
- University of Southern California, Stevens Neuroimaging and Informatics Institute, Keck School of Medicine, Laboratory of Neuro Imaging, Los Angeles, California, United States
| | - Derek B. Archer
- Vanderbilt University Medical Center, Vanderbilt Memory and Alzheimer’s Center, Nashville, Tennessee, United States
- Vanderbilt University Medical Center, Vanderbilt Genetics Institute, Nashville, Tennessee, United States
| | - Timothy J. Hohman
- Vanderbilt University Medical Center, Vanderbilt Memory and Alzheimer’s Center, Nashville, Tennessee, United States
- Vanderbilt University Medical Center, Vanderbilt Genetics Institute, Nashville, Tennessee, United States
| | - Bennett A. Landman
- Vanderbilt University, Department of Electrical and Computer Engineering, Nashville, Tennessee, United States
- Vanderbilt University, Department of Computer Science, Nashville, Tennessee, United States
- Vanderbilt University Medical Center, Department of Radiology and Radiological Sciences, Nashville, Tennessee, United States
| | - Zhiyuan Li
- Vanderbilt University, Department of Electrical and Computer Engineering, Nashville, Tennessee, United States
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10
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Zhang D, Zong F, Zhang Q, Yue Y, Zhang F, Zhao K, Wang D, Wang P, Zhang X, Liu Y. Anat-SFSeg: Anatomically-guided superficial fiber segmentation with point-cloud deep learning. Med Image Anal 2024; 95:103165. [PMID: 38608510 DOI: 10.1016/j.media.2024.103165] [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: 09/29/2023] [Revised: 03/28/2024] [Accepted: 04/05/2024] [Indexed: 04/14/2024]
Abstract
Diffusion magnetic resonance imaging (dMRI) tractography is a critical technique to map the brain's structural connectivity. Accurate segmentation of white matter, particularly the superficial white matter (SWM), is essential for neuroscience and clinical research. However, it is challenging to segment SWM due to the short adjacent gyri connection in a U-shaped pattern. In this work, we propose an Anatomically-guided Superficial Fiber Segmentation (Anat-SFSeg) framework to improve the performance on SWM segmentation. The framework consists of a unique fiber anatomical descriptor (named FiberAnatMap) and a deep learning network based on point-cloud data. The spatial coordinates of fibers represented as point clouds, as well as the anatomical features at both the individual and group levels, are fed into a neural network. The network is trained on Human Connectome Project (HCP) datasets and tested on the subjects with a range of cognitive impairment levels. One new metric named fiber anatomical region proportion (FARP), quantifies the ratio of fibers in the defined brain regions and enables the comparison with other methods. Another metric named anatomical region fiber count (ARFC), represents the average fiber number in each cluster for the assessment of inter-subject differences. The experimental results demonstrate that Anat-SFSeg achieves the highest accuracy on HCP datasets and exhibits great generalization on clinical datasets. Diffusion tensor metrics and ARFC show disorder severity associated alterations in patients with Alzheimer's disease (AD) and mild cognitive impairments (MCI). Correlations with cognitive grades show that these metrics are potential neuroimaging biomarkers for AD. Furthermore, Anat-SFSeg could be utilized to explore other neurodegenerative, neurodevelopmental or psychiatric disorders.
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Affiliation(s)
- Di Zhang
- School of Airtificial Intelligence, Beijing University of Posts and Telecommunications, Beijing, China
| | - Fangrong Zong
- School of Airtificial Intelligence, Beijing University of Posts and Telecommunications, Beijing, China.
| | - Qichen Zhang
- School of Airtificial Intelligence, Beijing University of Posts and Telecommunications, Beijing, China
| | - Yunhui Yue
- School of Airtificial Intelligence, Beijing University of Posts and Telecommunications, Beijing, China
| | - Fan Zhang
- School of Information and Communication Engineering, University of Electronic Science and Technology of China, Chengdu, China
| | - Kun Zhao
- School of Airtificial Intelligence, Beijing University of Posts and Telecommunications, Beijing, China
| | - Dawei Wang
- Department of Radiology, Qilu Hospital of Shandong University, Jinan, China; Department of Epidemiology and Health Statistics, School of Public Health, Shandong University, Jinan, China; Institute of Brain and Brain-Inspired Science, Shandong University, Jinan, China
| | - Pan Wang
- Department of Neurology, Tianjin Huanhu Hospital, Tianjin, China
| | - Xi Zhang
- Department of Neurology, the Second Medical Centre, National Clinical Research Centre for Geriatric Diseases, Chinese PLA General Hospital, Beijing, China
| | - Yong Liu
- School of Airtificial Intelligence, Beijing University of Posts and Telecommunications, Beijing, China
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11
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Betz AK, Cetin-Karayumak S, Bonke EM, Seitz-Holland J, Zhang F, Pieper S, O'Donnell LJ, Tripodis Y, Rathi Y, Shenton ME, Koerte IK. Executive functioning, behavior, and white matter microstructure in the chronic phase after pediatric mild traumatic brain injury: results from the adolescent brain cognitive development study. Psychol Med 2024; 54:2133-2143. [PMID: 38497117 DOI: 10.1017/s0033291724000229] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 03/19/2024]
Abstract
BACKGROUND Mild traumatic brain injury (mTBI) is common in children. Long-term cognitive and behavioral outcomes as well as underlying structural brain alterations following pediatric mTBI have yet to be determined. In addition, the effect of age-at-injury on long-term outcomes is largely unknown. METHODS Children with a history of mTBI (n = 406; Mage = 10 years, SDage = 0.63 years) who participated in the Adolescent Brain Cognitive Development (ABCD) study were matched (1:2 ratio) with typically developing children (TDC; n = 812) and orthopedic injury (OI) controls (n = 812). Task-based executive functioning, parent-rated executive functioning and emotion-regulation, and self-reported impulsivity were assessed cross-sectionally. Regression models were used to examine the effect of mTBI on these domains. The effect of age-at-injury was assessed by comparing children with their first mTBI at either 0-3, 4-7, or 8-10 years to the respective matched TDC controls. Fractional anisotropy (FA) and mean diffusivity (MD), both MRI-based measures of white matter microstructure, were compared between children with mTBI and controls. RESULTS Children with a history of mTBI displayed higher parent-rated executive dysfunction, higher impulsivity, and poorer self-regulation compared to both control groups. At closer investigation, these differences to TDC were only present in one respective age-at-injury group. No alterations were found in task-based executive functioning or white matter microstructure. CONCLUSIONS Findings suggest that everyday executive function, impulsivity, and emotion-regulation are affected years after pediatric mTBI. Outcomes were specific to the age at which the injury occurred, suggesting that functioning is differently affected by pediatric mTBI during vulnerable periods. Groups did not differ in white matter microstructure.
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Affiliation(s)
- Anja K Betz
- cBRAIN, Department of Child and Adolescent Psychiatry, Psychosomatics and Psychotherapy, Ludwig-Maximilians-Universität, Munich, Germany
| | - Suheyla Cetin-Karayumak
- Psychiatry Neuroimaging Laboratory, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
| | - Elena M Bonke
- cBRAIN, Department of Child and Adolescent Psychiatry, Psychosomatics and Psychotherapy, Ludwig-Maximilians-Universität, Munich, Germany
- Graduate School of Systemic Neurosciences, Ludwig-Maximilians-Universität, Munich, Germany
| | - Johanna Seitz-Holland
- Psychiatry Neuroimaging Laboratory, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
- Department of Psychiatry, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
| | - Fan Zhang
- Department of Radiology, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
| | | | - Lauren J O'Donnell
- Department of Radiology, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
| | - Yorghos Tripodis
- Department of Biostatistics, Boston University School of Public Health, Boston, MA, USA
| | - Yogesh Rathi
- Psychiatry Neuroimaging Laboratory, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
- Department of Psychiatry, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
- Department of Radiology, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
| | - Martha E Shenton
- Psychiatry Neuroimaging Laboratory, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
- Department of Psychiatry, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
- Department of Radiology, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
| | - Inga K Koerte
- cBRAIN, Department of Child and Adolescent Psychiatry, Psychosomatics and Psychotherapy, Ludwig-Maximilians-Universität, Munich, Germany
- Psychiatry Neuroimaging Laboratory, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
- Graduate School of Systemic Neurosciences, Ludwig-Maximilians-Universität, Munich, Germany
- Department of Psychiatry, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
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12
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He Q, Li R, Zhong N, Ma J, Nie F, Zhang R. The role and molecular mechanisms of the early growth response 3 gene in schizophrenia. Am J Med Genet B Neuropsychiatr Genet 2024; 195:e32969. [PMID: 38327141 DOI: 10.1002/ajmg.b.32969] [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: 07/04/2023] [Revised: 01/15/2024] [Accepted: 01/22/2024] [Indexed: 02/09/2024]
Abstract
Schizophrenia is a chronic, debilitating mental illness caused by both genetic and environmental factors. Genetic factors play a major role in schizophrenia development. Early growth response 3 (EGR3) is a member of the EGR family, which is associated with schizophrenia. Accumulating studies have investigated the relationship between EGR3 and schizophrenia. However, the role of EGR3 in schizophrenia pathogenesis remains unclear. In the present review, we focus on the progress of research related to the role of EGR3 in schizophrenia, including association studies between EGR3 and schizophrenia, abnormal gene expressional analysis of EGR3 in schizophrenia, biological function studies of EGR3 in schizophrenia, the molecular regulatory mechanism of EGR3 and schizophrenia susceptibility candidate genes, and possible role of EGR3 in the immune system function in schizophrenia. In summary, EGR3 is a schizophrenia risk candidate factor and has comprehensive regulatory roles in schizophrenia pathogenesis. Further studies investigating the molecular mechanisms of EGR3 in schizophrenia are warranted for understanding the pathophysiology of this disorder as well as the development of new therapeutic strategies for the treatment and control of this disorder.
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Affiliation(s)
- Qi He
- School of Basic Medicine, Shaanxi Key Laboratory of Acupuncture and Medicine, Shannxi University of Chinese Medicine, Xianyang, Shaanxi, China
| | - Ruochun Li
- Department of Medical Technology, Guiyang Healthcare Vocational University, Guiyang, Guizhou, China
| | - Nannan Zhong
- Department of Medical Technology, Guiyang Healthcare Vocational University, Guiyang, Guizhou, China
| | - Jie Ma
- Department of Electron Microscope, School of Basic Medical Sciences, Xi'an Jiaotong University Health Science Center, Xi'an, Shaanxi, China
| | - Fayi Nie
- School of Basic Medicine, Shaanxi Key Laboratory of Acupuncture and Medicine, Shannxi University of Chinese Medicine, Xianyang, Shaanxi, China
| | - Rui Zhang
- Department of Medical Technology, Guiyang Healthcare Vocational University, Guiyang, Guizhou, China
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13
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Tabata K, Son S, Miyata J, Toriumi K, Miyashita M, Suzuki K, Itokawa M, Takahashi H, Murai T, Arai M. Association of homocysteine with white matter dysconnectivity in schizophrenia. SCHIZOPHRENIA (HEIDELBERG, GERMANY) 2024; 10:39. [PMID: 38509166 PMCID: PMC10954654 DOI: 10.1038/s41537-024-00458-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/24/2023] [Accepted: 03/11/2024] [Indexed: 03/22/2024]
Abstract
Several studies have shown white matter (WM) dysconnectivity in people with schizophrenia (SZ). However, the underlying mechanism remains unclear. We investigated the relationship between plasma homocysteine (Hcy) levels and WM microstructure in people with SZ using diffusion tensor imaging (DTI). Fifty-three people with SZ and 83 healthy controls (HC) were included in this retrospective observational study. Tract-Based Spatial Statistics (TBSS) were used to evaluate group differences in WM microstructure. A significant negative correlation between plasma Hcy levels and WM microstructural disruption was noted in the SZ group (Spearman's ρ = -.330, P = 0.016) but not in the HC group (Spearman's ρ = .041, P = 0.712). These results suggest that increased Hcy may be associated with WM dysconnectivity in SZ, and the interaction between Hcy and WM dysconnectivity could be a potential mechanism of the pathophysiology of SZ. Further, longitudinal studies are required to investigate whether high Hcy levels subsequently cause WM microstructural disruption in people with SZ.
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Grants
- 19K17061 Ministry of Education, Culture, Sports, Science and Technology (MEXT)
- 18H02749 Ministry of Education, Culture, Sports, Science and Technology (MEXT)
- 18H05130 Ministry of Education, Culture, Sports, Science and Technology (MEXT)
- 20H05064 Ministry of Education, Culture, Sports, Science and Technology (MEXT)
- 23H04979 Ministry of Education, Culture, Sports, Science and Technology (MEXT)
- 21H02849 Ministry of Education, Culture, Sports, Science and Technology (MEXT)
- 21H05173 Ministry of Education, Culture, Sports, Science and Technology (MEXT)
- 23H02844 Ministry of Education, Culture, Sports, Science and Technology (MEXT)
- JP18dm0307008 Japan Agency for Medical Research and Development (AMED)
- JP21uk1024002 Japan Agency for Medical Research and Development (AMED)
- JPMJCR22P3 MEXT | JST | Core Research for Evolutional Science and Technology (CREST)
- The Novartis Pharma Research Grant; SENSHIN Medical Research Foundation; SUZUKEN Memorial Foundation; the Takeda Science Foundation.
- the Brain/MINDS Beyond program (23dm0307008) from the Japan Agency for Medical Research
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Affiliation(s)
- Koichi Tabata
- Schizophrenia Research Project, Department of Psychiatry and Behavioral Sciences, Tokyo Metropolitan Institute of Medical Science, Tokyo, Japan
- Department of Psychiatry and Behavioral Sciences, Graduate School of Medical and Dental Sciences, Tokyo Medical and Dental University, Tokyo, Japan
| | - Shuraku Son
- Department of Psychiatry, Graduate School of Medicine, Kyoto University, Kyoto, Japan.
| | - Jun Miyata
- Department of Psychiatry, Graduate School of Medicine, Kyoto University, Kyoto, Japan
| | - Kazuya Toriumi
- Schizophrenia Research Project, Department of Psychiatry and Behavioral Sciences, Tokyo Metropolitan Institute of Medical Science, Tokyo, Japan
| | - Mitsuhiro Miyashita
- Unit for Mental Health Promotion, Research Center for Social Science & Medicine, Tokyo Metropolitan Institute of Medical Science, Tokyo, Japan
| | - Kazuhiro Suzuki
- Schizophrenia Research Project, Department of Psychiatry and Behavioral Sciences, Tokyo Metropolitan Institute of Medical Science, Tokyo, Japan
- Department of Psychiatry, Shinshu University School of Medicine, Matsumoto, Japan
| | - Masanari Itokawa
- Schizophrenia Research Project, Department of Psychiatry and Behavioral Sciences, Tokyo Metropolitan Institute of Medical Science, Tokyo, Japan
- Department of Psychiatry, Tokyo Metropolitan Matsuzawa Hospital, Tokyo, Japan
| | - Hidehiko Takahashi
- Department of Psychiatry and Behavioral Sciences, Graduate School of Medical and Dental Sciences, Tokyo Medical and Dental University, Tokyo, Japan
- Center for Brain Integration Research, Tokyo Medical and Dental University, Tokyo, Japan
| | - Toshiya Murai
- Department of Psychiatry, Graduate School of Medicine, Kyoto University, Kyoto, Japan
| | - Makoto Arai
- Schizophrenia Research Project, Department of Psychiatry and Behavioral Sciences, Tokyo Metropolitan Institute of Medical Science, Tokyo, Japan
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14
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Yu K, Zhou H, Chen Z, Lei Y, Wu J, Yuan Q, He J. Mechanism of cognitive impairment and white matter damage in the MK-801 mice model of schizophrenia treated with quetiapine. Behav Brain Res 2024; 461:114838. [PMID: 38157989 DOI: 10.1016/j.bbr.2023.114838] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/15/2023] [Revised: 12/11/2023] [Accepted: 12/23/2023] [Indexed: 01/03/2024]
Abstract
Schizophrenia has been linked to cognitive impairment and white matter damage in a growing number of studies this year. In this study, we used the MK-801-induced schizophrenia-like mice model to investigate the effects of quetiapine on behavioral changes and myelin loss in the model mice. The subjects selected for this study were C57B6/J male mice, MK-801 (1 mg/kg/d intraperitoneal injection) modeling for 1 week and quetiapine (10 mg/kg/d intraperitoneal injection) treatment for 2 weeks. Behavioral tests were then performed using the three-chamber paradigm test and the Y maze test. Moreover, western blot, immunohistochemistry, and immunofluorescence were conducted to investigate the changes in oligodendrocyte spectrum markers. In addition, we performed some mechanism-related proteins by western blot. Quetiapine ameliorated cognitive impairment and cerebral white matter damage in MK-801 model mice, and the mechanism may be related to the PI3K/AKT pathways. The present study suggests that quetiapine has a possible mechanism for treating cognitive impairment and white matter damage caused by schizophrenia.
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Affiliation(s)
- Kai Yu
- School of Mental Health and the Affiliated Kangning Hospital, Wenzhou Key Laboratory for Basic and Translational Research in Mental Health, Zhejiang Provincial Clinical Research Center for Mental Disorders, Institute of Aging, Key Laboratory of Alzheimer's Disease of Zhejiang Province, Wenzhou Medical University, Wenzhou, Zhejiang, China
| | - Han Zhou
- School of Mental Health and the Affiliated Kangning Hospital, Wenzhou Key Laboratory for Basic and Translational Research in Mental Health, Zhejiang Provincial Clinical Research Center for Mental Disorders, Institute of Aging, Key Laboratory of Alzheimer's Disease of Zhejiang Province, Wenzhou Medical University, Wenzhou, Zhejiang, China
| | - Zhuo Chen
- School of Mental Health and the Affiliated Kangning Hospital, Wenzhou Key Laboratory for Basic and Translational Research in Mental Health, Zhejiang Provincial Clinical Research Center for Mental Disorders, Institute of Aging, Key Laboratory of Alzheimer's Disease of Zhejiang Province, Wenzhou Medical University, Wenzhou, Zhejiang, China
| | - Yuying Lei
- School of Mental Health and the Affiliated Kangning Hospital, Wenzhou Key Laboratory for Basic and Translational Research in Mental Health, Zhejiang Provincial Clinical Research Center for Mental Disorders, Institute of Aging, Key Laboratory of Alzheimer's Disease of Zhejiang Province, Wenzhou Medical University, Wenzhou, Zhejiang, China
| | - Junnan Wu
- School of Mental Health and the Affiliated Kangning Hospital, Wenzhou Key Laboratory for Basic and Translational Research in Mental Health, Zhejiang Provincial Clinical Research Center for Mental Disorders, Institute of Aging, Key Laboratory of Alzheimer's Disease of Zhejiang Province, Wenzhou Medical University, Wenzhou, Zhejiang, China
| | - Qianfa Yuan
- Xiamen Xian Yue Hospital, Xiamen, Fujian, China
| | - Jue He
- School of Mental Health and the Affiliated Kangning Hospital, Wenzhou Key Laboratory for Basic and Translational Research in Mental Health, Zhejiang Provincial Clinical Research Center for Mental Disorders, Institute of Aging, Key Laboratory of Alzheimer's Disease of Zhejiang Province, Wenzhou Medical University, Wenzhou, Zhejiang, China; Institute of Neurological Disease, First Affiliated Hospital, Henan University, Kaifeng, Henan, China.
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15
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Cetin-Karayumak S, Zhang F, Zurrin R, Billah T, Zekelman L, Makris N, Pieper S, O'Donnell LJ, Rathi Y. Harmonized diffusion MRI data and white matter measures from the Adolescent Brain Cognitive Development Study. Sci Data 2024; 11:249. [PMID: 38413633 PMCID: PMC10899197 DOI: 10.1038/s41597-024-03058-w] [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: 05/25/2023] [Accepted: 02/12/2024] [Indexed: 02/29/2024] Open
Abstract
The Adolescent Brain Cognitive Development (ABCD) Study® has collected data from over 10,000 children across 21 sites, providing insights into adolescent brain development. However, site-specific scanner variability has made it challenging to use diffusion MRI (dMRI) data from this study. To address this, a dataset of harmonized and processed ABCD dMRI data (from release 3) has been created, comprising quality-controlled imaging data from 9,345 subjects, focusing exclusively on the baseline session, i.e., the first time point of the study. This resource required substantial computational time (approx. 50,000 CPU hours) for harmonization, whole-brain tractography, and white matter parcellation. The dataset includes harmonized dMRI data, 800 white matter clusters, 73 anatomically labeled white matter tracts in full and low resolution, and 804 different dMRI-derived measures per subject (72.3 TB total size). Accessible via the NIMH Data Archive, it offers a large-scale dMRI dataset for studying structural connectivity in child and adolescent neurodevelopment. Additionally, several post-harmonization experiments were conducted to demonstrate the success of the harmonization process on the ABCD dataset.
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Affiliation(s)
- Suheyla Cetin-Karayumak
- Department of Psychiatry, Brigham and Women's Hospital, Harvard Medical School, Boston, Massachusetts, USA.
- Department of Psychiatry, Massachusetts General Hospital, Harvard Medical School, Boston, Massachusetts, USA.
| | - Fan Zhang
- Department of Radiology, Brigham and Women's Hospital, Harvard Medical School, Boston, Massachusetts, USA
| | - Ryan Zurrin
- Department of Psychiatry, Brigham and Women's Hospital, Harvard Medical School, Boston, Massachusetts, USA
| | - Tashrif Billah
- Department of Psychiatry, Brigham and Women's Hospital, Harvard Medical School, Boston, Massachusetts, USA
| | - Leo Zekelman
- Department of Neurosurgery, Brigham and Women's Hospital, Harvard Medical School, Boston, Massachusetts, USA
- Program in Speech and Hearing Bioscience and Technology, Division of Medical Sciences, Harvard University, Boston, Massachusetts, USA
| | - Nikos Makris
- Department of Psychiatry, Brigham and Women's Hospital, Harvard Medical School, Boston, Massachusetts, USA
- Department of Psychiatry, Massachusetts General Hospital, Harvard Medical School, Boston, Massachusetts, USA
| | | | - Lauren J O'Donnell
- Department of Radiology, Brigham and Women's Hospital, Harvard Medical School, Boston, Massachusetts, USA.
| | - Yogesh Rathi
- Department of Psychiatry, Brigham and Women's Hospital, Harvard Medical School, Boston, Massachusetts, USA
- Department of Psychiatry, Massachusetts General Hospital, Harvard Medical School, Boston, Massachusetts, USA
- Department of Radiology, Brigham and Women's Hospital, Harvard Medical School, Boston, Massachusetts, USA
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16
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Antenucci N, D'Errico G, Fazio F, Nicoletti F, Bruno V, Battaglia G. Changes in kynurenine metabolites in the gray and white matter of the dorsolateral prefrontal cortex of individuals affected by schizophrenia. SCHIZOPHRENIA (HEIDELBERG, GERMANY) 2024; 10:27. [PMID: 38413629 PMCID: PMC10899223 DOI: 10.1038/s41537-024-00447-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/19/2023] [Accepted: 02/02/2024] [Indexed: 02/29/2024]
Abstract
Alterations in the kynurenine pathway of tryptophan metabolism have been implicated in the pathophysiology of schizophrenia. Here, we performed an in-depth analysis of all metabolites of the kynurenine pathway, i.e., tryptophan (TRY), kynurenic acid (KYNA), L-kynurenine (KYN), 3-hydroxykynurenine (3-HK), anthranylic acid (ANA), 3-hydroxyanthranylic acid (3-HANA), xanthurenic acid (XA) and quinolinic acid (QUINA), in postmortem samples of the dorsolateral prefrontal cortex (DLPFC, Brodmann area 46, 9) of individuals affected by schizophrenia and non-schizophrenic controls. The analysis was carried out in the gray and white matter. Levels of KYN, 3-HK, ANA, and 3-HANA were significantly increased in both the gray and white matter of the DLPFC of individuals affected by schizophrenia, whereas levels of TRY, KYNA, and QUINA were increased exclusively in the white matter and remained unchanged in the gray matter. These increases in kynurenine metabolites did not correlate with age, sex, duration of the disease, and duration and type of antipsychotic medication. These findings suggest that the two major branches of the kynurenine pathway, i.e., the transamination of KYN into KYNA, and hydroxylation of KYN into 3-HK are activated in the white matter of individuals affected by schizophrenia, perhaps as a result of neuroinflammation, and support the evidence that abnormalities of the white matter are consistenly associated with schizophrenia.
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Affiliation(s)
- Nico Antenucci
- Department of Physiology and Pharmacology, Sapienza University, Rome, Italy
- Department of Pharmacology and Neuroscience, Texas Tech University Health Sciences Center, Lubbock, TX, USA
| | | | - Francesco Fazio
- IRCCS Neuromed, Pozzilli, Italy
- Department of Psychiatry, University of California San Diego, La Jolla, CA, USA
| | - Ferdinando Nicoletti
- Department of Physiology and Pharmacology, Sapienza University, Rome, Italy
- IRCCS Neuromed, Pozzilli, Italy
| | - Valeria Bruno
- Department of Physiology and Pharmacology, Sapienza University, Rome, Italy.
- IRCCS Neuromed, Pozzilli, Italy.
| | - Giuseppe Battaglia
- Department of Physiology and Pharmacology, Sapienza University, Rome, Italy
- IRCCS Neuromed, Pozzilli, Italy
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17
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Wang S, Li T, Zhao B, Dai W, Yao Y, Li C, Li T, Zhu H, Zhang H. Identification and validation of supervariants reveal novel loci associated with human white matter microstructure. Genome Res 2024; 34:20-33. [PMID: 38190638 PMCID: PMC10904010 DOI: 10.1101/gr.277905.123] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/18/2023] [Accepted: 12/05/2023] [Indexed: 01/10/2024]
Abstract
As an essential part of the central nervous system, white matter coordinates communications between different brain regions and is related to a wide range of neurodegenerative and neuropsychiatric disorders. Previous genome-wide association studies (GWASs) have uncovered loci associated with white matter microstructure. However, GWASs suffer from limited reproducibility and difficulties in detecting multi-single-nucleotide polymorphism (multi-SNP) and epistatic effects. In this study, we adopt the concept of supervariants, a combination of alleles in multiple loci, to account for potential multi-SNP effects. We perform supervariant identification and validation to identify loci associated with 22 white matter fractional anisotropy phenotypes derived from diffusion tensor imaging. To increase reproducibility, we use United Kingdom (UK) Biobank White British (n = 30,842) data for discovery and internal validation, and UK Biobank White but non-British (n = 1927) data, Europeans from the Adolescent Brain Cognitive Development study (n = 4399) data, and Europeans from the Human Connectome Project (n = 319) data for external validation. We identify 23 novel loci on the discovery set that have not been reported in the previous GWASs on white matter microstructure. Among them, three supervariants on genomic regions 5q35.1, 8p21.2, and 19q13.32 have P-values lower than 0.05 in the meta-analysis of the three independent validation data sets. These supervariants contain genetic variants located in genes that have been related to brain structures, cognitive functions, and neuropsychiatric diseases. Our findings provide a better understanding of the genetic architecture underlying white matter microstructure.
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Affiliation(s)
- Shiying Wang
- Department of Biostatistics, Yale School of Public Health, New Haven, Connecticut 06510, USA
| | - Ting Li
- Department of Applied Mathematics, The Hong Kong Polytechnic University, Hong Kong, China
| | - Bingxin Zhao
- Department of Statistics and Data Science, University of Pennsylvania, Philadelphia, Pennsylvania 19104-1686, USA
| | - Wei Dai
- Department of Biostatistics, Yale School of Public Health, New Haven, Connecticut 06510, USA
| | - Yisha Yao
- Department of Biostatistics, Yale School of Public Health, New Haven, Connecticut 06510, USA
| | - Cai Li
- Department of Biostatistics, St. Jude Children's Research Hospital, Memphis, Tennessee 38105, USA
| | - Tengfei Li
- Department of Radiology, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina 27599, USA
- Biomedical Research Imaging Center, School of Medicine, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina 27514, USA
| | - Hongtu Zhu
- Department of Biostatistics, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina 27599, USA
| | - Heping Zhang
- Department of Biostatistics, Yale School of Public Health, New Haven, Connecticut 06510, USA;
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18
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Wang P, Jiang Y, Biswal BB. Aberrant interhemispheric structural and functional connectivity within whole brain in schizophrenia. Schizophr Res 2024; 264:336-344. [PMID: 38218019 DOI: 10.1016/j.schres.2023.12.033] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/19/2022] [Revised: 11/27/2023] [Accepted: 12/26/2023] [Indexed: 01/15/2024]
Abstract
OBJECTIVE Schizophrenia is a serious mental disorder whose etiology remains unclear. Although numerous studies have analyzed the abnormal gray matter functional activity and whole-brain anatomical changes in schizophrenia, fMRI signal fluctuations from white matter have usually been ignored and rarely reported in the literature. METHODS We employed 45 schizophrenia subjects and 75 healthy controls (HCs) from a publicly available fMRI dataset. By combining the voxel-mirrored homotopic connectivity (VMHC) measure and fiber tracking method, we investigated the interhemispheric functional and structural connectivity within whole brain in schizophrenia. RESULTS Compared to HCs, patients with schizophrenia exhibited significantly reduced VMHC in the bilateral middle occipital gyrus, precentral gyrus, postcentral gyrus and corpus callosum. Fiber tracking results showed the changes in structural connectivity for the bilateral precentral gyrus, and the bilateral corpus callosum, and the fiber bundles connecting bilateral precentral gyrus and connecting the bilateral corpus callosum passed through the posterior midbody, isthmus and splenium of mid-sagittal corpus callosum, which closely related to the interhemispheric integration of visual and auditory information. More importantly, we observed a negative correlation between averaged VMHC values in the postcentral gyrus and SAPS scores, and a positive correlation between the fractional anisotropy of fiber bundle connecting the bilateral precentral gyrus and Matrix Reasoning scores in schizophrenia. CONCLUSION Our findings provide a novel perspective of white matter functional images on understanding abnormal interhemispheric visual and auditory information transfer in schizophrenia.
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Affiliation(s)
- Pan Wang
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Laboratory for Neuroinformation, Center for Information in Medicine, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, China
| | - Yuan Jiang
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Laboratory for Neuroinformation, Center for Information in Medicine, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, China
| | - Bharat B Biswal
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Laboratory for Neuroinformation, Center for Information in Medicine, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, China; Department of Biomedical Engineering, New Jersey Institute of Technology, Newark, NJ 07102, USA.
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19
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Klaassen AL, Michel C, Stüble M, Kaess M, Morishima Y, Kindler J. Reduced anterior callosal white matter in risk for psychosis associated with processing speed as a fundamental cognitive impairment. Schizophr Res 2024; 264:211-219. [PMID: 38157681 DOI: 10.1016/j.schres.2023.12.026] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/17/2023] [Revised: 11/29/2023] [Accepted: 12/17/2023] [Indexed: 01/03/2024]
Abstract
BACKGROUND Previous research in psychotic disorders discovered associations between reduced integrity of white matter (WM) in the corpus callosum (CC) and impaired cognitive functions, suggesting processing speed as a central construct. However, it is still largely unexplored to what extent disruption in callosal WM is related to cognitive deficits during the risk stage prior to psychosis. METHODS To address this gap, we measured the WM integrity in CC by fractional anisotropy (FA) and assessed cognition in 60 clinical-high risk for psychosis (CHR) patients during adolescence/young adulthood and 38 healthy control (HC) subjects. We employed tract based spatial statistics to examine group differences and associations between CC-FA and processing speed, executive function, and spatial working memory. RESULTS We revealed deficits in processing speed, executive function, and spatial working memory of CHR patients, and reductions in FA of the genu and the body of the CC (p < 0.05, corrected for multiple comparisons) compared to HC. A mediation analysis using the combined sample (CHR + HC) showed that processing speed mediates the associations between the impaired CC structure and executive function and spatial working memory, respectively. Exploratory analyses between CC-FA and the cognitive domains located associations of processing speed in the genu and the body of CC with distinct spatial distributions of executive function and spatial working memory. CONCLUSION We suggest processing speed as a subordinate cognitive factor contributing to the associations between callosal WM, executive function and working memory. These results extend findings in psychotic disorders to the prior risk stage.
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Affiliation(s)
- Arndt-Lukas Klaassen
- University Hospital of Child and Adolescent Psychiatry and Psychotherapy Bern, University of Bern, Switzerland.
| | - Chantal Michel
- University Hospital of Child and Adolescent Psychiatry and Psychotherapy Bern, University of Bern, Switzerland
| | - Miriam Stüble
- University Hospital of Child and Adolescent Psychiatry and Psychotherapy Bern, University of Bern, Switzerland; Graduate School for Health Sciences, University of Bern, Bern, Switzerland
| | - Michael Kaess
- University Hospital of Child and Adolescent Psychiatry and Psychotherapy Bern, University of Bern, Switzerland; University Hospital Heidelberg, Department of Child and Adolescent Psychiatry, Center for Psychosocial Medicine, University of Heidelberg, Germany
| | - Yosuke Morishima
- University Hospital of Psychiatry Bern, Department of Psychiatric Neurophysiology, University of Bern, Switzerland
| | - Jochen Kindler
- University Hospital of Child and Adolescent Psychiatry and Psychotherapy Bern, University of Bern, Switzerland
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20
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Kobayashi H, Sasabayashi D, Takahashi T, Furuichi A, Kido M, Takayanagi Y, Noguchi K, Suzuki M. The relationship between gray/white matter contrast and cognitive performance in first-episode schizophrenia. Cereb Cortex 2024; 34:bhae009. [PMID: 38265871 DOI: 10.1093/cercor/bhae009] [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: 02/10/2023] [Revised: 01/04/2024] [Accepted: 01/04/2024] [Indexed: 01/26/2024] Open
Abstract
Previous postmortem brain studies have revealed disturbed myelination in the intracortical regions in patients with schizophrenia, possibly reflecting anomalous brain maturational processes. However, it currently remains unclear whether this anomalous myelination is already present in early illness stages and/or progresses during the course of the illness. In this magnetic resonance imaging study, we examined gray/white matter contrast (GWC) as a potential marker of intracortical myelination in 63 first-episode schizophrenia (FESz) patients and 77 healthy controls (HC). Furthermore, we investigated the relationships between GWC findings and clinical/cognitive variables in FESz patients. GWC in the bilateral temporal, parietal, occipital, and insular regions was significantly higher in FESz patients than in HC, which was partly associated with the durations of illness and medication, the onset age, and lower executive and verbal learning performances. Because higher GWC implicates lower myelin in the deeper layers of the cortex, these results suggest that schizophrenia patients have less intracortical myelin at the time of their first psychotic episode, which underlies lower cognitive performance in early illness stages.
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Affiliation(s)
- Haruko Kobayashi
- Department of Neuropsychiatry, University of Toyama Graduate School of Medicine and Pharmaceutical Sciences, 2630 Sugitani, Toyama 930-0194, Japan
- Research Center for idling Brain Science, University of Toyama, 2630 Sugitani, Toyama, 930-0194, Japan
| | - Daiki Sasabayashi
- Department of Neuropsychiatry, University of Toyama Graduate School of Medicine and Pharmaceutical Sciences, 2630 Sugitani, Toyama 930-0194, Japan
- Research Center for idling Brain Science, University of Toyama, 2630 Sugitani, Toyama, 930-0194, Japan
| | - Tsutomu Takahashi
- Department of Neuropsychiatry, University of Toyama Graduate School of Medicine and Pharmaceutical Sciences, 2630 Sugitani, Toyama 930-0194, Japan
- Research Center for idling Brain Science, University of Toyama, 2630 Sugitani, Toyama, 930-0194, Japan
| | - Atsushi Furuichi
- Department of Neuropsychiatry, University of Toyama Graduate School of Medicine and Pharmaceutical Sciences, 2630 Sugitani, Toyama 930-0194, Japan
- Research Center for idling Brain Science, University of Toyama, 2630 Sugitani, Toyama, 930-0194, Japan
| | - Mikio Kido
- Department of Neuropsychiatry, University of Toyama Graduate School of Medicine and Pharmaceutical Sciences, 2630 Sugitani, Toyama 930-0194, Japan
- Kido Clinic, 244 Honoki, Imizu City, Toyama, 934-0053, Japan
| | - Yoichiro Takayanagi
- Department of Neuropsychiatry, University of Toyama Graduate School of Medicine and Pharmaceutical Sciences, 2630 Sugitani, Toyama 930-0194, Japan
- Arisawabashi Hospital, 5-5 Hane-Shin, Fuchu-Machi, Toyama, 939-2704, Japan
| | - Kyo Noguchi
- Department of Radiology, University of Toyama Graduate School of Medicine and Pharmaceutical Sciences, 2630 Sugitani, Toyama, 930-0194, Japan
| | - Michio Suzuki
- Department of Neuropsychiatry, University of Toyama Graduate School of Medicine and Pharmaceutical Sciences, 2630 Sugitani, Toyama 930-0194, Japan
- Research Center for idling Brain Science, University of Toyama, 2630 Sugitani, Toyama, 930-0194, Japan
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21
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Romero-Miguel D, Casquero-Veiga M, Lamanna-Rama N, Torres-Sánchez S, MacDowell KS, García-Partida JA, Santa-Marta C, Berrocoso E, Leza JC, Desco M, Soto-Montenegro ML. N-acetylcysteine during critical neurodevelopmental periods prevents behavioral and neurochemical deficits in the Poly I:C rat model of schizophrenia. Transl Psychiatry 2024; 14:14. [PMID: 38191622 PMCID: PMC10774365 DOI: 10.1038/s41398-023-02652-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/04/2023] [Revised: 10/24/2023] [Accepted: 11/06/2023] [Indexed: 01/10/2024] Open
Abstract
Schizophrenia is a chronic neurodevelopmental disorder with an inflammatory/prooxidant component. N-acetylcysteine (NAC) has been evaluated in schizophrenia as an adjuvant to antipsychotics, but its role as a preventive strategy has not been sufficiently explored. We aimed to evaluate the potential of NAC administration in two-time windows before the onset of symptoms in a schizophrenia-like maternal immune stimulation (MIS) rat model. Pregnant Wistar rats were injected with Poly I:C or Saline on gestational day (GD) 15. Three different preventive approaches were evaluated: 1) NAC treatment during periadolescence in the offspring (from postnatal day [PND] 35 to 49); 2) NAC treatment during pregnancy after MIS challenge until delivery (GD15-21); and 3) NAC treatment throughout all pregnancy (GD1-21). At postnatal day (PND) 70, prepulse inhibition (PPI) and anxiety levels were evaluated. In vivo magnetic resonance (MR) imaging was acquired on PND100 to assess structural changes in gray and white matter, and brain metabolite concentrations. Additionally, inflammation and oxidative stress (IOS) markers were measured ex vivo in selected brain regions. MIS offspring showed behavioral, neuroanatomical, and biochemical alterations. Interestingly, NAC treatment during periadolescence prevented PPI deficits and partially counteracted some biochemical imbalances. Moreover, NAC treatments during pregnancy not only replicated the beneficial outcomes reported by the treatment in periadolescence, but also prevented some neuroanatomical deficits, including reductions in hippocampal and corpus callosum volumes. This study suggests that early reduction of inflammation and prooxidation could help prevent the onset of schizophrenia-like symptoms, supporting the importance of anti-IOS compounds in ameliorating this disorder.
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Grants
- MLS was supported by the Ministerio de Ciencia e Innovación, Instituto de Salud Carlos III (project number PI17/01766, and grant number BA21/00030), co-financed by the European Regional Development Fund (ERDF), “A way to make Europe”; project PID2021-128862OB-I00 funded by MCIN /AEI /10.13039/501100011033 / FEDER, UE, CIBER de Salud Mental - Instituto de Salud Carlos III (project number CB07/09/0031); Delegación del Gobierno para el Plan Nacional sobre Drogas (project number 2017/085, 2022/008917); and Fundación Alicia Koplowitz.
- DRM was supported by Consejería de Educación e investigación, Comunidad de Madrid, co-funded by the European Social Fund “Investing in your future” (grant, PEJD-2018-PRE/BMD-7899).
- MCV was supported by a predoctoral grant from Fundación Tatiana Pérez de Guzmán el Bueno.
- NLR was supported by the Instituto de investigación Sanitaria Gregorio Marañón, “Programa Intramural de Impulso a la I+D+I 2019”.
- EBD, JAG-P and ST-S work was supported by the “Fondo Europeo de Desarrollo Regional” (FEDER)-UE “A way to build Europe” from the “Ministerio de Economía y Competitividad” (RTI2018-099778-B-I00); from the “Plan Nacional sobre Drogas, Ministerio de Sanidad, Consumo y Bienestar Social” (2019I041); from the “Ministerio de Salud-Instituto de Salud Carlos III” (PI18/01691); from the “Programa Operativo de Andalucía FEDER, Iniciativa Territorial Integrada ITI 2014-2020 Consejería Salud y Familias, Junta de Andalucía” (PI-0080-2017, PI-0009-2017), "Consejería de Salud y Familias, Junta de Andalucía" (PI-0134-2018 and PEMP-0008-2020); from the "Consejería de Transformación Económica, Industria, Conocimiento y Universidad, Junta de Andalucía" (P20_00958 and CTS-510); from the CEIMAR (CEIJ-003); from the “Instituto de Investigación e Innovación en Ciencias Biomédicas de Cádiz-INiBICA” (LI19/06IN-CO22; IN-C09); from the “CIBERSAM”: CIBER-Consorcio Centro de Investigación Biomédica en Red- (CB07/09/0033), Instituto de Salud Carlos III, Ministerio de Ciencia e Innovación and from the European Union’s Horizon 2020 research and innovation programme under the Marie Sklodowska-Curie grant agreement No 955684.
- JCL was supported by the Ministerio de Economía y Competitividad, MINECO-EU-FEDER (SAF2016-75500-R) and Ministerio de Ciencia e Innovación (PID2019-109033RB-I00).
- MD work was supported by Ministerio de Ciencia e Innovación (MCIN) and Instituto de Salud Carlos III (PT20/00044). The CNIC is supported by the Instituto de Salud Carlos III (ISCIII), the Ministerio de Ciencia e Innovación (MCIN) and the Pro CNIC Foundation, and is a Severo Ochoa Center of Excellence (SEV-2015-0505).
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Affiliation(s)
- Diego Romero-Miguel
- Instituto de Investigación Sanitaria Gregorio Marañón, Madrid, 28007, Spain
- Department of Bioengineering, Universidad Carlos III de Madrid, Leganés (Madrid), 28911, Spain
| | - Marta Casquero-Veiga
- Instituto de Investigación Sanitaria Gregorio Marañón, Madrid, 28007, Spain
- Instituto de Investigación Sanitaria Fundación Jiménez Díaz, IIS-FJD, 28040, Madrid, Spain
- Cardiovascular Imaging and Population Studies, Centro Nacional de Investigaciones Cardiovasculares (CNIC), 28029, Madrid, Spain
| | - Nicolás Lamanna-Rama
- Instituto de Investigación Sanitaria Gregorio Marañón, Madrid, 28007, Spain
- Department of Bioengineering, Universidad Carlos III de Madrid, Leganés (Madrid), 28911, Spain
| | - Sonia Torres-Sánchez
- CIBER de Salud Mental (CIBERSAM), Madrid, 28029, Spain
- Neuropsychopharmacology & Psychobiology Research Group, Department of Neuroscience, Universidad de Cádiz, Cádiz, 11003, Spain
- Instituto de Investigación e Innovación en Ciencias Biomédicas de Cádiz (INiBICA), Hospital Universitario Puerta del Mar, Cádiz, 11009, Spain
| | - Karina S MacDowell
- CIBER de Salud Mental (CIBERSAM), Madrid, 28029, Spain
- Department of Pharmacology & Toxicology, School of Medicine, Universidad Complutense (UCM), IIS Imas12, IUIN, Madrid, 28040, Spain
| | - José A García-Partida
- Neuropsychopharmacology & Psychobiology Research Group, Department of Neuroscience, Universidad de Cádiz, Cádiz, 11003, Spain
- Instituto de Investigación e Innovación en Ciencias Biomédicas de Cádiz (INiBICA), Hospital Universitario Puerta del Mar, Cádiz, 11009, Spain
| | | | - Esther Berrocoso
- CIBER de Salud Mental (CIBERSAM), Madrid, 28029, Spain
- Neuropsychopharmacology & Psychobiology Research Group, Department of Neuroscience, Universidad de Cádiz, Cádiz, 11003, Spain
- Instituto de Investigación e Innovación en Ciencias Biomédicas de Cádiz (INiBICA), Hospital Universitario Puerta del Mar, Cádiz, 11009, Spain
| | - Juan C Leza
- CIBER de Salud Mental (CIBERSAM), Madrid, 28029, Spain
- Department of Pharmacology & Toxicology, School of Medicine, Universidad Complutense (UCM), IIS Imas12, IUIN, Madrid, 28040, Spain
| | - Manuel Desco
- Instituto de Investigación Sanitaria Gregorio Marañón, Madrid, 28007, Spain.
- Department of Bioengineering, Universidad Carlos III de Madrid, Leganés (Madrid), 28911, Spain.
- CIBER de Salud Mental (CIBERSAM), Madrid, 28029, Spain.
- Advanced Imaging Unit, Centro Nacional de Investigaciones Cardiovasculares (CNIC), Madrid, 28029, Spain.
| | - María Luisa Soto-Montenegro
- Instituto de Investigación Sanitaria Gregorio Marañón, Madrid, 28007, Spain.
- CIBER de Salud Mental (CIBERSAM), Madrid, 28029, Spain.
- Grupo de Fisiopatología y Farmacología del Sistema Digestivo de la Universidad Rey Juan Carlos (NeuGut), Alcorcón (Madrid), 28922, Spain.
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Van Dyken PC, MacKinley M, Khan AR, Palaniyappan L. Cortical Network Disruption Is Minimal in Early Stages of Psychosis. SCHIZOPHRENIA BULLETIN OPEN 2024; 5:sgae010. [PMID: 39144115 PMCID: PMC11207789 DOI: 10.1093/schizbullopen/sgae010] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Indexed: 08/16/2024]
Abstract
Background and Hypothesis Schizophrenia is associated with white matter disruption and topological reorganization of cortical connectivity but the trajectory of these changes, from the first psychotic episode to established illness, is poorly understood. Current studies in first-episode psychosis (FEP) patients using diffusion magnetic resonance imaging (dMRI) suggest such disruption may be detectable at the onset of psychosis, but specific results vary widely, and few reports have contextualized their findings with direct comparison to young adults with established illness. Study Design Diffusion and T1-weighted 7T MR scans were obtained from N = 112 individuals (58 with untreated FEP, 17 with established schizophrenia, 37 healthy controls) recruited from London, Ontario. Voxel- and network-based analyses were used to detect changes in diffusion microstructural parameters. Graph theory metrics were used to probe changes in the cortical network hierarchy and to assess the vulnerability of hub regions to disruption. The analysis was replicated with N = 111 (57 patients, 54 controls) from the Human Connectome Project-Early Psychosis (HCP-EP) dataset. Study Results Widespread microstructural changes were found in people with established illness, but changes in FEP patients were minimal. Unlike the established illness group, no appreciable topological changes in the cortical network were observed in FEP patients. These results were replicated in the early psychosis patients of the HCP-EP datasets, which were indistinguishable from controls in most metrics. Conclusions The white matter structural changes observed in established schizophrenia are not a prominent feature in the early stages of this illness.
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Affiliation(s)
- Peter C Van Dyken
- Neuroscience Graduate Program, Schulich School of Medicine and Dentistry, Western University, London, ON, Canada
| | - Michael MacKinley
- Lawson Health Research Institute, London Health Sciences Centre, London, ON, Canada
| | - Ali R Khan
- Robarts Research Institute, Schulich School of Medicine and Dentistry, Western University, London, ON, Canada
- Department of Medical Biophysics, Schulich School of Medicine and Dentistry, Western University, London, ON, Canada
| | - Lena Palaniyappan
- Robarts Research Institute, Schulich School of Medicine and Dentistry, Western University, London, ON, Canada
- Department of Medical Biophysics, Schulich School of Medicine and Dentistry, Western University, London, ON, Canada
- Department of Psychiatry, Douglas Mental Health University Institute, McGill University, London, ON, Canada
- Department of Psychiatry, Schulich School of Medicine and Dentistry, Western University, London, ON, Canada
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Falkai P, Rossner MJ, Raabe FJ, Wagner E, Keeser D, Maurus I, Roell L, Chang E, Seitz-Holland J, Schulze TG, Schmitt A. Disturbed Oligodendroglial Maturation Causes Cognitive Dysfunction in Schizophrenia: A New Hypothesis. Schizophr Bull 2023; 49:1614-1624. [PMID: 37163675 PMCID: PMC10686333 DOI: 10.1093/schbul/sbad065] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 05/12/2023]
Abstract
BACKGROUND AND HYPOTHESIS Cognitive impairment is a hallmark of schizophrenia, but no effective treatment is available to date. The underlying pathophysiology includes disconnectivity between hippocampal and prefrontal brain regions. Supporting evidence comes from diffusion-weighted imaging studies that suggest abnormal organization of frontotemporal white matter pathways in schizophrenia. STUDY DESIGN Here, we hypothesize that in schizophrenia, deficient maturation of oligodendrocyte precursor cells (OPCs) into mature oligodendrocytes substantially contributes to abnormal frontotemporal macro- and micro-connectivity and subsequent cognitive deficits. STUDY RESULTS Our postmortem studies indicate a reduced oligodendrocyte number in the cornu ammonis 4 (CA4) subregion of the hippocampus, and others have reported the same histopathological finding in the dorsolateral prefrontal cortex. Our series of studies on aerobic exercise training showed a volume increase in the hippocampus, specifically in the CA4 region, and improved cognition in individuals with schizophrenia. The cognitive effects were subsequently confirmed by meta-analyses. Cell-specific schizophrenia polygenic risk scores showed that exercise-induced CA4 volume increase significantly correlates with OPCs. From animal models, it is evident that early life stress and oligodendrocyte-related gene variants lead to schizophrenia-related behavior, cognitive deficits, impaired oligodendrocyte maturation, and reduced myelin thickness. CONCLUSIONS Based on these findings, we propose that pro-myelinating drugs (e.g., the histamine blocker clemastine) combined with aerobic exercise training may foster the regeneration of myelin plasticity as a basis for restoring frontotemporal connectivity and cognition in schizophrenia.
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Affiliation(s)
- Peter Falkai
- Department of Psychiatry and Psychotherapy, University Hospital, Ludwig-Maximilian University, Munich, Germany
- Max-Planck-Institute of Psychiatry, Munich, Germany
| | - Moritz J Rossner
- Department of Psychiatry and Psychotherapy, University Hospital, Ludwig-Maximilian University, Munich, Germany
| | - Florian J Raabe
- Department of Psychiatry and Psychotherapy, University Hospital, Ludwig-Maximilian University, Munich, Germany
| | - Elias Wagner
- Department of Psychiatry and Psychotherapy, University Hospital, Ludwig-Maximilian University, Munich, Germany
| | - Daniel Keeser
- Department of Psychiatry and Psychotherapy, University Hospital, Ludwig-Maximilian University, Munich, Germany
- NeuroImaging Core Unit Munich (NICUM), University Hospital, Ludwig-Maximilian University, Munich, Germany
| | - Isabel Maurus
- Department of Psychiatry and Psychotherapy, University Hospital, Ludwig-Maximilian University, Munich, Germany
| | - Lukas Roell
- Department of Psychiatry and Psychotherapy, University Hospital, Ludwig-Maximilian University, Munich, Germany
- NeuroImaging Core Unit Munich (NICUM), University Hospital, Ludwig-Maximilian University, Munich, Germany
| | - Emily Chang
- Department of Psychiatry and Psychotherapy, University Hospital, Ludwig-Maximilian University, Munich, Germany
| | - Johanna Seitz-Holland
- Department of Psychiatry, Brigham and Women’s Hospital, Harvard Medical School, Boston, MA, USA
| | - Thomas G Schulze
- Institute for Psychiatric Phenomic and Genomic (IPPG), Munich, Germany
| | - Andrea Schmitt
- Department of Psychiatry and Psychotherapy, University Hospital, Ludwig-Maximilian University, Munich, Germany
- Laboratory of Neuroscience (LIM27), Institute of Psychiatry, University of São Paulo (USP), São Paulo-SP, Brazil
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Pieciak T, París G, Beck D, Maximov II, Tristán-Vega A, de Luis-García R, Westlye LT, Aja-Fernández S. Spherical means-based free-water volume fraction from diffusion MRI increases non-linearly with age in the white matter of the healthy human brain. Neuroimage 2023; 279:120324. [PMID: 37574122 DOI: 10.1016/j.neuroimage.2023.120324] [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: 04/02/2023] [Revised: 08/08/2023] [Accepted: 08/10/2023] [Indexed: 08/15/2023] Open
Abstract
The term free-water volume fraction (FWVF) refers to the signal fraction that could be found as the cerebrospinal fluid of the brain, which has been demonstrated as a sensitive measure that correlates with cognitive performance and various neuropathological processes. It can be quantified by properly fitting the isotropic component of the magnetic resonance (MR) signal in diffusion-sensitized sequences. Using N=287 healthy subjects (178F/109M) aged 25-94, this study examines in detail the evolution of the FWVF obtained with the spherical means technique from multi-shell acquisitions in the human brain white matter across the adult lifespan, which has been previously reported to exhibit a positive trend when estimated from single-shell data using the bi-tensor signal representation. We found evidence of a noticeably non-linear gain after the sixth decade of life, with a region-specific variate and varying change rate of the spherical means-based multi-shell FWVF parameter with age, at the same time, a heteroskedastic pattern across the adult lifespan is suggested. On the other hand, the FW corrected diffusion tensor imaging (DTI) leads to a region-dependent flattened age-related evolution of the mean diffusivity (MD) and fractional anisotropy (FA), along with a considerable reduction in their variability, as compared to the studies conducted over the standard (single-component) DTI. This way, our study provides a new perspective on the trajectory-based assessment of the brain and explains the conceivable reason for the variations observed in FA and MD parameters across the lifespan with previous studies under the standard diffusion tensor imaging.
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Affiliation(s)
- Tomasz Pieciak
- Laboratorio de Procesado de Imagen (LPI), ETSI Telecomunicación, Universidad de Valladolid, Valladolid, Spain.
| | - Guillem París
- Laboratorio de Procesado de Imagen (LPI), ETSI Telecomunicación, Universidad de Valladolid, Valladolid, Spain
| | - Dani Beck
- Department of Psychology, University of Oslo, Oslo, Norway; NORMENT, Division of Mental Health and Addiction, Oslo University Hospital & Institute of Clinical Medicine, University of Oslo, Oslo, Norway; Department of Psychiatric Research, Diakonhjemmet Hospital, Oslo, Norway. https://twitter.com/_DaniBeck
| | - Ivan I Maximov
- NORMENT, Division of Mental Health and Addiction, Oslo University Hospital & Institute of Clinical Medicine, University of Oslo, Oslo, Norway; Department of Health and Functioning, Western Norway University of Applied Sciences, Bergen, Norway
| | - Antonio Tristán-Vega
- Laboratorio de Procesado de Imagen (LPI), ETSI Telecomunicación, Universidad de Valladolid, Valladolid, Spain
| | - Rodrigo de Luis-García
- Laboratorio de Procesado de Imagen (LPI), ETSI Telecomunicación, Universidad de Valladolid, Valladolid, Spain
| | - Lars T Westlye
- Department of Psychology, University of Oslo, Oslo, Norway; NORMENT, Division of Mental Health and Addiction, Oslo University Hospital & Institute of Clinical Medicine, University of Oslo, Oslo, Norway; KG Jebsen Centre for Neurodevelopmental Disorders, University of Oslo, Oslo, Norway. https://twitter.com/larswestlye
| | - Santiago Aja-Fernández
- Laboratorio de Procesado de Imagen (LPI), ETSI Telecomunicación, Universidad de Valladolid, Valladolid, Spain. https://twitter.com/SantiagoAjaFer1
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Kang IC, Pasternak O, Zhang F, Penzel N, Seitz-Holland J, Tang Y, Zhang T, Xu L, Li H, Keshavan M, Whitfield-Gabrielli S, Niznikiewicz M, Stone W, Wang J, Shenton M. Microstructural Cortical Gray Matter Changes Preceding Accelerated Volume Changes in Individuals at Clinical High Risk for Psychosis. RESEARCH SQUARE 2023:rs.3.rs-3179575. [PMID: 37841868 PMCID: PMC10571628 DOI: 10.21203/rs.3.rs-3179575/v1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 10/17/2023]
Abstract
Recent studies show that accelerated cortical gray matter (GM) volume reduction seen in anatomical MRI can help distinguish between individuals at clinical high risk (CHR) for psychosis who will develop psychosis and those who will not. This reduction is thought to result from an accumulation of microstructural changes, such as decreased spine density and dendritic arborization. Detecting the microstructural sources of these changes before they accumulate is crucial, as volume reduction likely indicates an underlying neurodegenerative process. Our study aimed to detect these microstructural GM alterations using diffusion MRI (dMRI). We tested for baseline and longitudinal group differences in anatomical and dMRI data from 160 individuals at CHR and 96 healthy controls (HC) acquired in a single imaging site. Eight cortical lobes were examined for GM volume and GM microstructure. A novel dMRI measure, interstitial free water (iFW), was used to quantify GM microstructure by eliminating cerebrospinal fluid contribution. Additionally, we assessed whether these measures differentiated the 33 individuals at CHR who developed psychosis (CHR-P) from the 127 individuals at CHR who did not (CHR-NP). At baseline the CHR group had significantly higher iFW than HC in the prefrontal, temporal, parietal, and occipital lobes, while volume was reduced only in the temporal lobe. Neither iFW nor volume differentiated between the CHR-P and CHR-NP groups at baseline. However, in most brain areas, the CHR-P group demonstrated significantly accelerated iFW increase and volume reduction with time than the CHR-NP group. Our results demonstrate that microstructural GM changes in individuals at CHR have a wider extent than volumetric changes and they predate the acceleration of brain changes that occur around psychosis onset. Microstructural GM changes are thus an early pathology at the prodromal stage of psychosis that may be useful for early detection and a better mechanistic understanding of psychosis development.
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Affiliation(s)
| | | | | | | | - Johanna Seitz-Holland
- Brigham and Women's Hospital and Massachusetts General Hospital, Harvard Medical School
| | - Yingying Tang
- Shanghai Mental Health Center, Shanghai Jiao Tong University School of Medicine
| | - Tianhong Zhang
- Shanghai Mental Health Center, Shanghai Jiaotong University School of Medicine, Shanghai Key Laboratory of Psychotic Disorders, Shanghai 200030, PR China
| | | | | | | | | | | | | | - Jijun Wang
- Shanghai Key Laboratory of Psychotic Disorders, Shanghai Mental Health Center, Shanghai Jiao Tong University School of Medicine
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26
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Huang J, Zhao Y, Tian Z, Qu W, Du X, Zhang J, Tan Y, Wang Z, Tan S. Evaluating the clinical utility of speech analysis and machine learning in schizophrenia: A pilot study. Comput Biol Med 2023; 164:107359. [PMID: 37591160 DOI: 10.1016/j.compbiomed.2023.107359] [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: 04/25/2023] [Revised: 07/04/2023] [Accepted: 08/12/2023] [Indexed: 08/19/2023]
Abstract
BACKGROUND Schizophrenia is a serious mental disorder that significantly impacts social functioning and quality of life. However, current diagnostic methods lack objective biomarker support. While some studies have indicated differences in audio features between patients with schizophrenia and healthy controls, these findings are influenced by demographic information and variations in experimental paradigms. Therefore, it is crucial to explore stable and reliable audio biomarkers for an auxiliary diagnosis and disease severity prediction of schizophrenia. METHOD A total of 130 individuals (65 patients with schizophrenia and 65 healthy controls) read three fixed texts containing positive, neutral, and negative emotions, and recorded them. All audio signals were preprocessed and acoustic features were extracted by a librosa-0.9.2 toolkit. Independent sample t-tests were performed on two sets of acoustic features, and Pearson correlation on the acoustic features and Positive and Negative Syndrome Scale (PANSS) scores of the schizophrenia group. Classification algorithms in scikit-learn were used to diagnose schizophrenia and predict the level of negative symptoms. RESULTS Significant differences were observed between the two groups in the mfcc_8, mfcc_11, and mfcc_33 of mel-frequency cepstral coefficient (MFCC). Furthermore, a significant correlation was found between mfcc_7 and the negative PANSS scores. Through acoustic features, we could not only differentiate patients with schizophrenia from healthy controls with an accuracy of 0.815 but also predict the grade of the negative symptoms in schizophrenia with an average accuracy of 0.691. CONCLUSIONS The results demonstrated the considerable potential of acoustic characteristics as reliable biomarkers for diagnosing schizophrenia and predicting clinical symptoms.
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Affiliation(s)
- Jie Huang
- Beijing HuiLongGuan Hospital, Peking University HuiLongGuan Clinical Medical School, Beijing, 100096, China
| | - Yanli Zhao
- Beijing HuiLongGuan Hospital, Peking University HuiLongGuan Clinical Medical School, Beijing, 100096, China
| | - Zhanxiao Tian
- Beijing HuiLongGuan Hospital, Peking University HuiLongGuan Clinical Medical School, Beijing, 100096, China
| | - Wei Qu
- Beijing HuiLongGuan Hospital, Peking University HuiLongGuan Clinical Medical School, Beijing, 100096, China
| | - Xia Du
- Beijing HuiLongGuan Hospital, Peking University HuiLongGuan Clinical Medical School, Beijing, 100096, China
| | - Jie Zhang
- Beijing HuiLongGuan Hospital, Peking University HuiLongGuan Clinical Medical School, Beijing, 100096, China
| | - Yunlong Tan
- Beijing HuiLongGuan Hospital, Peking University HuiLongGuan Clinical Medical School, Beijing, 100096, China
| | - Zhiren Wang
- Beijing HuiLongGuan Hospital, Peking University HuiLongGuan Clinical Medical School, Beijing, 100096, China
| | - Shuping Tan
- Beijing HuiLongGuan Hospital, Peking University HuiLongGuan Clinical Medical School, Beijing, 100096, China.
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27
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Joo SW, Jo YT, Ahn S, Choi YJ, Choi W, Kim SK, Joe S, Lee J. Structural impairment in superficial and deep white matter in schizophrenia. Acta Neuropsychiatr 2023:1-10. [PMID: 37620164 DOI: 10.1017/neu.2023.44] [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] [Indexed: 08/26/2023]
Abstract
OBJECTIVE Although disconnectivity among brain regions has been one of the main hypotheses for schizophrenia, the superficial white matter (SWM) has received less attention in schizophrenia research than the deep white matter (DWM) owing to the challenge of consistent reconstruction across subjects. METHODS We obtained the diffusion magnetic resonance imaging (dMRI) data of 223 healthy controls and 143 patients with schizophrenia. After harmonising the raw dMRIs from three different studies, we performed whole-brain two-tensor tractography and fibre clustering on the tractography data. We compared the fractional anisotropy (FA) of white matter tracts between healthy controls and patients with schizophrenia. Spearman's rho was adopted for the associations with clinical symptoms measured by the Positive and Negative Syndrome Scale (PANSS). The Bonferroni correction was used to adjust multiple testing. RESULTS Among the 33 DWM and 8 SWM tracts, patients with schizophrenia had a lower FA in 14 DWM and 4 SWM tracts than healthy controls, with small effect sizes. In the patient group, the FA deviations of the corticospinal and superficial-occipital tracts were negatively correlated with the PANSS negative score; however, this correlation was not evident after adjusting for multiple testing. CONCLUSION We observed the structural impairments of both the DWM and SWM tracts in patients with schizophrenia. The SWM could be a potential target of interest in future research on neural biomarkers for schizophrenia.
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Affiliation(s)
- Sung Woo Joo
- Department of Psychiatry, Asan Medical Center, University of Ulsan College of Medicine, Seoul, Republic of Korea
| | - Young Tak Jo
- Department of Psychiatry, Kangdong Sacred Heart Hospital, Hallym University College of Medicine, Seoul, Republic of Korea
| | - Soojin Ahn
- Department of Psychiatry, Asan Medical Center, University of Ulsan College of Medicine, Seoul, Republic of Korea
| | - Young Jae Choi
- Department of Psychiatry, Asan Medical Center, University of Ulsan College of Medicine, Seoul, Republic of Korea
| | - Woohyeok Choi
- Department of Psychiatry, Asan Medical Center, University of Ulsan College of Medicine, Seoul, Republic of Korea
| | - Sang Kyoung Kim
- Department of Psychiatry, Asan Medical Center, University of Ulsan College of Medicine, Seoul, Republic of Korea
| | - Soohyun Joe
- Brain Laboratory, Department of Psychiatry, University of California San Diego, School of Medicine, San Diego, CA, USA
| | - Jungsun Lee
- Department of Psychiatry, Asan Medical Center, University of Ulsan College of Medicine, Seoul, Republic of Korea
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28
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Newlin NR, Kim ME, Kanakaraj P, Yao T, Hohman T, Pechman KR, Beason-Held LL, Resnick SM, Archer D, Jefferson A, Landman BA, Moyer D. MidRISH: Unbiased harmonization of rotationally invariant harmonics of the diffusion signal. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.08.12.553099. [PMID: 37645973 PMCID: PMC10462069 DOI: 10.1101/2023.08.12.553099] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 08/31/2023]
Abstract
Objective Data harmonization is necessary for removing confounding effects in multi-site diffusion image analysis. One such harmonization method, LinearRISH, scales rotationally invariant spherical harmonic (RISH) features from one site ("target") to the second ("reference") to reduce confounding scanner effects. However, reference and target site designations are not arbitrary and resultant diffusion metrics (fractional anisotropy, mean diffusivity) are biased by this choice. In this work we propose MidRISH: rather than scaling reference RISH features to target RISH features, we project both sites to a mid-space. Methods We validate MidRISH with the following experiments: harmonizing scanner differences from 37 matched patients free of cognitive impairment, and harmonizing acquisition and study differences on 117 matched patients free of cognitive impairment. Conclusion MidRISH reduces bias of reference selection while preserving harmonization efficacy of LinearRISH. Significance Users should be cautious when performing LinearRISH harmonization. To select a reference site is to choose diffusion metric effect-size. Our proposed method eliminates the bias-inducing site selection step.
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Affiliation(s)
- Nancy R Newlin
- Department of Computer Science at Vanderbilt University, Nashville, TN, USA
| | - Michael E Kim
- Department of Computer Science at Vanderbilt University, Nashville, TN, USA
| | | | - Tianyuan Yao
- Department of Computer Science at Vanderbilt University, Nashville, TN, USA
| | - Timothy Hohman
- VMAC, VUMC, Nashville, TN, USA and Vanderbilt University, Nashville, TN, USA
| | | | - Lori L Beason-Held
- Laboratory of Behavioral Neuroscience, National Institute on Aging, National Institutes of Health, Baltimore, MD, USA
| | - Susan M Resnick
- Laboratory of Behavioral Neuroscience, National Institute on Aging, National Institutes of Health, Baltimore, MD, USA
| | - Derek Archer
- VMAC, VUMC, Nashville, TN, USA and Vanderbilt University, Nashville, TN, USA
| | - Angela Jefferson
- VMAC, VUMC, Nashville, TN, USA and Vanderbilt University, Nashville, TN, USA
| | - Bennett A Landman
- Department of Computer Science at Vanderbilt University, Nashville, TN, USA
| | - Daniel Moyer
- Department of Computer Science at Vanderbilt University, Nashville, TN, USA
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Martín-Hernández D, Muñoz-López M, Tendilla-Beltrán H, Caso JR, García-Bueno B, Menchén L, Leza JC. Immune System and Brain/Intestinal Barrier Functions in Psychiatric Diseases: Is Sphingosine-1-Phosphate at the Helm? Int J Mol Sci 2023; 24:12634. [PMID: 37628815 PMCID: PMC10454107 DOI: 10.3390/ijms241612634] [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: 07/27/2023] [Revised: 08/07/2023] [Accepted: 08/08/2023] [Indexed: 08/27/2023] Open
Abstract
Over the past few decades, extensive research has shed light on immune alterations and the significance of dysfunctional biological barriers in psychiatric disorders. The leaky gut phenomenon, intimately linked to the integrity of both brain and intestinal barriers, may play a crucial role in the origin of peripheral and central inflammation in these pathologies. Sphingosine-1-phosphate (S1P) is a bioactive lipid that regulates both the immune response and the permeability of biological barriers. Notably, S1P-based drugs, such as fingolimod and ozanimod, have received approval for treating multiple sclerosis, an autoimmune disease of the central nervous system (CNS), and ulcerative colitis, an inflammatory condition of the colon, respectively. Although the precise mechanisms of action are still under investigation, the effectiveness of S1P-based drugs in treating these pathologies sparks a debate on extending their use in psychiatry. This comprehensive review aims to delve into the molecular mechanisms through which S1P modulates the immune system and brain/intestinal barrier functions. Furthermore, it will specifically focus on psychiatric diseases, with the primary objective of uncovering the potential of innovative therapies based on S1P signaling.
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Affiliation(s)
- David Martín-Hernández
- Departamento de Farmacología y Toxicología, Facultad de Medicina, Universidad Complutense de Madrid (UCM), Instituto de Investigación Hospital 12 de Octubre (i+12), Instituto Universitario de Investigación en Neuroquímica (IUIN), 28040 Madrid, Spain; (M.M.-L.); (J.R.C.); (B.G.-B.); (J.C.L.)
- Centro de Investigación Biomédica en Red de Salud Mental, Instituto de Salud Carlos III (CIBERSAM, ISCIII), 28029 Madrid, Spain
| | - Marina Muñoz-López
- Departamento de Farmacología y Toxicología, Facultad de Medicina, Universidad Complutense de Madrid (UCM), Instituto de Investigación Hospital 12 de Octubre (i+12), Instituto Universitario de Investigación en Neuroquímica (IUIN), 28040 Madrid, Spain; (M.M.-L.); (J.R.C.); (B.G.-B.); (J.C.L.)
- Centro de Investigación Biomédica en Red de Salud Mental, Instituto de Salud Carlos III (CIBERSAM, ISCIII), 28029 Madrid, Spain
| | - Hiram Tendilla-Beltrán
- Laboratorio de Neuropsiquiatría, Instituto de Fisiología, Benemérita Universidad Autónoma de Puebla (BUAP), 72570 Puebla, Mexico;
| | - Javier R. Caso
- Departamento de Farmacología y Toxicología, Facultad de Medicina, Universidad Complutense de Madrid (UCM), Instituto de Investigación Hospital 12 de Octubre (i+12), Instituto Universitario de Investigación en Neuroquímica (IUIN), 28040 Madrid, Spain; (M.M.-L.); (J.R.C.); (B.G.-B.); (J.C.L.)
- Centro de Investigación Biomédica en Red de Salud Mental, Instituto de Salud Carlos III (CIBERSAM, ISCIII), 28029 Madrid, Spain
| | - Borja García-Bueno
- Departamento de Farmacología y Toxicología, Facultad de Medicina, Universidad Complutense de Madrid (UCM), Instituto de Investigación Hospital 12 de Octubre (i+12), Instituto Universitario de Investigación en Neuroquímica (IUIN), 28040 Madrid, Spain; (M.M.-L.); (J.R.C.); (B.G.-B.); (J.C.L.)
- Centro de Investigación Biomédica en Red de Salud Mental, Instituto de Salud Carlos III (CIBERSAM, ISCIII), 28029 Madrid, Spain
| | - Luis Menchén
- Servicio de Aparato Digestivo, Hospital General Universitario Gregorio Marañón, Departamento de Medicina, Universidad Complutense, Instituto de Investigación Sanitaria Gregorio Marañón, 28007 Madrid, Spain;
- Centro de Investigación Biomédica en Red de Enfermedades Hepáticas y Digestivas, Instituto de Salud Carlos III (CIBEREHD, ISCIII), 28029 Madrid, Spain
| | - Juan C. Leza
- Departamento de Farmacología y Toxicología, Facultad de Medicina, Universidad Complutense de Madrid (UCM), Instituto de Investigación Hospital 12 de Octubre (i+12), Instituto Universitario de Investigación en Neuroquímica (IUIN), 28040 Madrid, Spain; (M.M.-L.); (J.R.C.); (B.G.-B.); (J.C.L.)
- Centro de Investigación Biomédica en Red de Salud Mental, Instituto de Salud Carlos III (CIBERSAM, ISCIII), 28029 Madrid, Spain
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Porter A, Fei S, Damme KSF, Nusslock R, Gratton C, Mittal VA. A meta-analysis and systematic review of single vs. multimodal neuroimaging techniques in the classification of psychosis. Mol Psychiatry 2023; 28:3278-3292. [PMID: 37563277 PMCID: PMC10618094 DOI: 10.1038/s41380-023-02195-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/03/2022] [Revised: 07/11/2023] [Accepted: 07/17/2023] [Indexed: 08/12/2023]
Abstract
BACKGROUND Psychotic disorders are characterized by structural and functional abnormalities in brain networks. Neuroimaging techniques map and characterize such abnormalities using unique features (e.g., structural integrity, coactivation). However, it is unclear if a specific method, or a combination of modalities, is particularly effective in identifying differences in brain networks of someone with a psychotic disorder. METHODS A systematic meta-analysis evaluated machine learning classification of schizophrenia spectrum disorders in comparison to healthy control participants using various neuroimaging modalities (i.e., T1-weighted imaging (T1), diffusion tensor imaging (DTI), resting state functional connectivity (rs-FC), or some combination (multimodal)). Criteria for manuscript inclusion included whole-brain analyses and cross-validation to provide a complete picture regarding the predictive ability of large-scale brain systems in psychosis. For this meta-analysis, we searched Ovid MEDLINE, PubMed, PsychInfo, Google Scholar, and Web of Science published between inception and March 13th 2023. Prediction results were averaged for studies using the same dataset, but parallel analyses were run that included studies with pooled sample across many datasets. We assessed bias through funnel plot asymmetry. A bivariate regression model determined whether differences in imaging modality, demographics, and preprocessing methods moderated classification. Separate models were run for studies with internal prediction (via cross-validation) and external prediction. RESULTS 93 studies were identified for quantitative review (30 T1, 9 DTI, 40 rs-FC, and 14 multimodal). As a whole, all modalities reliably differentiated those with schizophrenia spectrum disorders from controls (OR = 2.64 (95%CI = 2.33 to 2.95)). However, classification was relatively similar across modalities: no differences were seen across modalities in the classification of independent internal data, and a small advantage was seen for rs-FC studies relative to T1 studies in classification in external datasets. We found large amounts of heterogeneity across results resulting in significant signs of bias in funnel plots and Egger's tests. Results remained similar, however, when studies were restricted to those with less heterogeneity, with continued small advantages for rs-FC relative to structural measures. Notably, in all cases, no significant differences were seen between multimodal and unimodal approaches, with rs-FC and unimodal studies reporting largely overlapping classification performance. Differences in demographics and analysis or denoising were not associated with changes in classification scores. CONCLUSIONS The results of this study suggest that neuroimaging approaches have promise in the classification of psychosis. Interestingly, at present most modalities perform similarly in the classification of psychosis, with slight advantages for rs-FC relative to structural modalities in some specific cases. Notably, results differed substantially across studies, with suggestions of biased effect sizes, particularly highlighting the need for more studies using external prediction and large sample sizes. Adopting more rigorous and systematized standards will add significant value toward understanding and treating this critical population.
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Affiliation(s)
- Alexis Porter
- Department of Psychology, Northwestern University, Evanston, IL, USA.
| | - Sihan Fei
- Department of Psychology, Northwestern University, Evanston, IL, USA
| | - Katherine S F Damme
- Department of Psychology, Northwestern University, Evanston, IL, USA
- Institute for Innovations in Developmental Sciences, Northwestern University, Evanston and Chicago, IL, USA
| | - Robin Nusslock
- Department of Psychology, Northwestern University, Evanston, IL, USA
| | - Caterina Gratton
- Department of Psychology, Florida State University, Tallahassee, FL, USA
| | - Vijay A Mittal
- Department of Psychology, Northwestern University, Evanston, IL, USA
- Institute for Innovations in Developmental Sciences, Northwestern University, Evanston and Chicago, IL, USA
- Department of Psychiatry, Northwestern University, Chicago, IL, USA
- Medical Social Sciences, Northwestern University, Chicago, IL, USA
- Institute for Policy Research, Northwestern University, Chicago, IL, USA
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31
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Li S, Sakurai K, Ohgidani M, Kato TA, Hikida T. Ameliorative effects of Fingolimod (FTY720) on microglial activation and psychosis-related behavior in short term cuprizone exposed mice. Mol Brain 2023; 16:59. [PMID: 37438826 DOI: 10.1186/s13041-023-01047-5] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/31/2022] [Accepted: 06/21/2023] [Indexed: 07/14/2023] Open
Abstract
Schizophrenia is a psychiatric disorder that affects around 1% of the population in widespread populations, with severe cases leading to long-term hospitalization and necessitation of lifelong treatment. Recent studies on schizophrenia have highlighted the involvement of inflammatory and immunoregulatory mechanisms with the onset of symptoms, and the usage of anti-inflammatory treatments are being tested against periods of rapid psychosis. In the central nervous system, microglia are the innate immune population which are activated in response to a wide range of physical and psychological stress factors and produce proinflammatory mediators such as cytokines. Microglial activation and neuroinflammation has been associated to numerous psychiatric disorders including schizophrenia, especially during psychotic episodes. Thus, novel treatments which dampen microglial activation may be of great relevance in the treatment of psychiatric disorders. Fingolimod (FTY720) is a drug used as an immunosuppressive treatment to multiple sclerosis. Recent clinical trials have focused on FTY720 as a treatment for the behavioral symptoms in schizophrenia. However, the mechanisms of Fingolimod in treating the symptoms of schizophrenia are not clear. In this study we use a recently developed neuroinflammatory psychosis model in mice: cuprizone short-term exposure, to investigate the effects of FTY720 administration. FTY720 administration was able to completely alleviate methamphetamine hypersensitivity caused by cuprizone exposure. Moreover, administration of FTY720 improved multiple measures of neuroinflammation (microglial activation, cytokine production, and leucocyte infiltration). In conclusion, our results highlight the future use of FTY720 as a direct anti-inflammatory treatment against microglial activation and psychosis.
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Affiliation(s)
- Siyao Li
- Laboratory for Advanced Brain Functions, Institute for Protein Research, Osaka University, Suita, Osaka, Japan
- Graduate School of Frontier Biosciences, Osaka University, Suita, Osaka, Japan
| | - Koki Sakurai
- Laboratory for Advanced Brain Functions, Institute for Protein Research, Osaka University, Suita, Osaka, Japan.
- Laboratory of Protein Profiling and Functional Proteomics, Institute for Protein Research, Osaka University, Suita, Osaka, Japan.
| | - Masahiro Ohgidani
- Department of Functional Anatomy and Neuroscience, Asahikawa Medical University, Hokkaido, Japan
- Department of Neuropsychiatry, Graduate School of Medical Sciences, Kyushu University, Fukuoka, Japan
| | - Takahiro A Kato
- Department of Neuropsychiatry, Graduate School of Medical Sciences, Kyushu University, Fukuoka, Japan.
| | - Takatoshi Hikida
- Laboratory for Advanced Brain Functions, Institute for Protein Research, Osaka University, Suita, Osaka, Japan.
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Hu F, Chen AA, Horng H, Bashyam V, Davatzikos C, Alexander-Bloch A, Li M, Shou H, Satterthwaite TD, Yu M, Shinohara RT. Image harmonization: A review of statistical and deep learning methods for removing batch effects and evaluation metrics for effective harmonization. Neuroimage 2023; 274:120125. [PMID: 37084926 PMCID: PMC10257347 DOI: 10.1016/j.neuroimage.2023.120125] [Citation(s) in RCA: 40] [Impact Index Per Article: 20.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/14/2023] [Revised: 04/12/2023] [Accepted: 04/19/2023] [Indexed: 04/23/2023] Open
Abstract
Magnetic resonance imaging and computed tomography from multiple batches (e.g. sites, scanners, datasets, etc.) are increasingly used alongside complex downstream analyses to obtain new insights into the human brain. However, significant confounding due to batch-related technical variation, called batch effects, is present in this data; direct application of downstream analyses to the data may lead to biased results. Image harmonization methods seek to remove these batch effects and enable increased generalizability and reproducibility of downstream results. In this review, we describe and categorize current approaches in statistical and deep learning harmonization methods. We also describe current evaluation metrics used to assess harmonization methods and provide a standardized framework to evaluate newly-proposed methods for effective harmonization and preservation of biological information. Finally, we provide recommendations to end-users to advocate for more effective use of current methods and to methodologists to direct future efforts and accelerate development of the field.
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Affiliation(s)
- Fengling Hu
- Penn Statistics in Imaging and Visualization Endeavor (PennSIVE), Department of Biostatistics, Epidemiology, and Informatics, Perelman School of Medicine, University of Pennsylvania, 423 Guardian Dr, Philadelphia, PA 19104, United States.
| | - Andrew A Chen
- Penn Statistics in Imaging and Visualization Endeavor (PennSIVE), Department of Biostatistics, Epidemiology, and Informatics, Perelman School of Medicine, University of Pennsylvania, 423 Guardian Dr, Philadelphia, PA 19104, United States
| | - Hannah Horng
- Penn Statistics in Imaging and Visualization Endeavor (PennSIVE), Department of Biostatistics, Epidemiology, and Informatics, Perelman School of Medicine, University of Pennsylvania, 423 Guardian Dr, Philadelphia, PA 19104, United States
| | - Vishnu Bashyam
- Center for Biomedical Image Computing and Analytics (CBICA), Perelman School of Medicine, United States
| | - Christos Davatzikos
- Center for Biomedical Image Computing and Analytics (CBICA), Perelman School of Medicine, United States
| | - Aaron Alexander-Bloch
- Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, United States; Penn-CHOP Lifespan Brain Institute, United States; Department of Child and Adolescent Psychiatry and Behavioral Science, Children's Hospital of Philadelphia, United States
| | - Mingyao Li
- Statistical Center for Single-Cell and Spatial Genomics, Perelman School of Medicine, University of Pennsylvania, United States
| | - Haochang Shou
- Penn Statistics in Imaging and Visualization Endeavor (PennSIVE), Department of Biostatistics, Epidemiology, and Informatics, Perelman School of Medicine, University of Pennsylvania, 423 Guardian Dr, Philadelphia, PA 19104, United States; Center for Biomedical Image Computing and Analytics (CBICA), Perelman School of Medicine, United States
| | - Theodore D Satterthwaite
- Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, United States; Penn-CHOP Lifespan Brain Institute, United States; The Penn Lifespan Informatics and Neuroimaging Center, Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, United States
| | - Meichen Yu
- Indiana Alzheimer's Disease Research Center, Indiana University School of Medicine, United States
| | - Russell T Shinohara
- Penn Statistics in Imaging and Visualization Endeavor (PennSIVE), Department of Biostatistics, Epidemiology, and Informatics, Perelman School of Medicine, University of Pennsylvania, 423 Guardian Dr, Philadelphia, PA 19104, United States; Center for Biomedical Image Computing and Analytics (CBICA), Perelman School of Medicine, United States
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33
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Seitz-Holland J, Nägele FL, Kubicki M, Pasternak O, Cho KIK, Hough M, Mulert C, Shenton ME, Crow TJ, James ACD, Lyall AE. Shared and distinct white matter abnormalities in adolescent-onset schizophrenia and adolescent-onset psychotic bipolar disorder. Psychol Med 2023; 53:4707-4719. [PMID: 35796024 PMCID: PMC11119277 DOI: 10.1017/s003329172200160x] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 11/06/2022]
Abstract
BACKGROUND While adolescent-onset schizophrenia (ADO-SCZ) and adolescent-onset bipolar disorder with psychosis (psychotic ADO-BPD) present a more severe clinical course than their adult forms, their pathophysiology is poorly understood. Here, we study potentially state- and trait-related white matter diffusion-weighted magnetic resonance imaging (dMRI) abnormalities along the adolescent-onset psychosis continuum to address this need. METHODS Forty-eight individuals with ADO-SCZ (20 female/28 male), 15 individuals with psychotic ADO-BPD (7 female/8 male), and 35 healthy controls (HCs, 18 female/17 male) underwent dMRI and clinical assessments. Maps of extracellular free-water (FW) and fractional anisotropy of cellular tissue (FAT) were compared between individuals with psychosis and HCs using tract-based spatial statistics and FSL's Randomise. FAT and FW values were extracted, averaged across all voxels that demonstrated group differences, and then utilized to test for the influence of age, medication, age of onset, duration of illness, symptom severity, and intelligence. RESULTS Individuals with adolescent-onset psychosis exhibited pronounced FW and FAT abnormalities compared to HCs. FAT reductions were spatially more widespread in ADO-SCZ. FW increases, however, were only present in psychotic ADO-BPD. In HCs, but not in individuals with adolescent-onset psychosis, FAT was positively related to age. CONCLUSIONS We observe evidence for cellular (FAT) and extracellular (FW) white matter abnormalities in adolescent-onset psychosis. Although cellular white matter abnormalities were more prominent in ADO-SCZ, such alterations may reflect a shared trait, i.e. neurodevelopmental pathology, present across the psychosis spectrum. Extracellular abnormalities were evident in psychotic ADO-BPD, potentially indicating a more dynamic, state-dependent brain reaction to psychosis.
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Affiliation(s)
- Johanna Seitz-Holland
- Psychiatry Neuroimaging Laboratory, Department of Psychiatry, Brigham and Women’s Hospital, Harvard Medical School, Boston, MA, USA
- Department of Psychiatry, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
| | - Felix L. Nägele
- Psychiatry Neuroimaging Laboratory, Department of Psychiatry, Brigham and Women’s Hospital, Harvard Medical School, Boston, MA, USA
- Psychiatry Neuroimaging Branch, Department of Psychiatry and Psychotherapy, University Medical Center Hamburg-Eppendorf, University of Hamburg, Hamburg, Germany
| | - Marek Kubicki
- Psychiatry Neuroimaging Laboratory, Department of Psychiatry, Brigham and Women’s Hospital, Harvard Medical School, Boston, MA, USA
- Department of Psychiatry, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
- Department of Radiology, Brigham and Women’s Hospital, Harvard Medical School, Boston, MA, USA
| | - Ofer Pasternak
- Psychiatry Neuroimaging Laboratory, Department of Psychiatry, Brigham and Women’s Hospital, Harvard Medical School, Boston, MA, USA
- Department of Radiology, Brigham and Women’s Hospital, Harvard Medical School, Boston, MA, USA
| | - Kang Ik K. Cho
- Psychiatry Neuroimaging Laboratory, Department of Psychiatry, Brigham and Women’s Hospital, Harvard Medical School, Boston, MA, USA
| | - Morgan Hough
- SANE POWIC, University Department of Psychiatry, Warneford Hospital, Oxford, UK
- Highfield Unit, University Department of Psychiatry, Warneford Hospital, Oxford, UK
| | - Christoph Mulert
- Psychiatry Neuroimaging Branch, Department of Psychiatry and Psychotherapy, University Medical Center Hamburg-Eppendorf, University of Hamburg, Hamburg, Germany
- Centre for Psychiatry and Psychotherapy, Justus-Liebig-University, Giessen, Germany
| | - Martha E. Shenton
- Psychiatry Neuroimaging Laboratory, Department of Psychiatry, Brigham and Women’s Hospital, Harvard Medical School, Boston, MA, USA
- Department of Psychiatry, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
- Department of Radiology, Brigham and Women’s Hospital, Harvard Medical School, Boston, MA, USA
| | - Timothy J. Crow
- SANE POWIC, University Department of Psychiatry, Warneford Hospital, Oxford, UK
| | - Anthony C. D. James
- SANE POWIC, University Department of Psychiatry, Warneford Hospital, Oxford, UK
- Highfield Unit, University Department of Psychiatry, Warneford Hospital, Oxford, UK
| | - Amanda E. Lyall
- Psychiatry Neuroimaging Laboratory, Department of Psychiatry, Brigham and Women’s Hospital, Harvard Medical School, Boston, MA, USA
- Department of Psychiatry, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
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34
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Korbmacher M, Gurholt TP, de Lange AMG, van der Meer D, Beck D, Eikefjord E, Lundervold A, Andreassen OA, Westlye LT, Maximov II. Bio-psycho-social factors' associations with brain age: a large-scale UK Biobank diffusion study of 35,749 participants. Front Psychol 2023; 14:1117732. [PMID: 37359862 PMCID: PMC10288151 DOI: 10.3389/fpsyg.2023.1117732] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/06/2022] [Accepted: 04/27/2023] [Indexed: 06/28/2023] Open
Abstract
Brain age refers to age predicted by brain features. Brain age has previously been associated with various health and disease outcomes and suggested as a potential biomarker of general health. Few previous studies have systematically assessed brain age variability derived from single and multi-shell diffusion magnetic resonance imaging data. Here, we present multivariate models of brain age derived from various diffusion approaches and how they relate to bio-psycho-social variables within the domains of sociodemographic, cognitive, life-satisfaction, as well as health and lifestyle factors in midlife to old age (N = 35,749, 44.6-82.8 years of age). Bio-psycho-social factors could uniquely explain a small proportion of the brain age variance, in a similar pattern across diffusion approaches: cognitive scores, life satisfaction, health and lifestyle factors adding to the variance explained, but not socio-demographics. Consistent brain age associations across models were found for waist-to-hip ratio, diabetes, hypertension, smoking, matrix puzzles solving, and job and health satisfaction and perception. Furthermore, we found large variability in sex and ethnicity group differences in brain age. Our results show that brain age cannot be sufficiently explained by bio-psycho-social variables alone. However, the observed associations suggest to adjust for sex, ethnicity, cognitive factors, as well as health and lifestyle factors, and to observe bio-psycho-social factor interactions' influence on brain age in future studies.
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Affiliation(s)
- Max Korbmacher
- Department of Health and Functioning, Western Norway University of Applied Sciences, Bergen, Norway
- Norwegian Centre for Mental Disorder Research (NORMENT), Division of Mental Health and Addiction, Oslo University Hospital, University of Oslo, Oslo, Norway
- Mohn Medical Imaging and Visualization Center (MMIV), Bergen, Norway
| | - Tiril P. Gurholt
- Norwegian Centre for Mental Disorder Research (NORMENT), Division of Mental Health and Addiction, Oslo University Hospital, University of Oslo, Oslo, Norway
| | - Ann-Marie G. de Lange
- Norwegian Centre for Mental Disorder Research (NORMENT), Division of Mental Health and Addiction, Oslo University Hospital, University of Oslo, Oslo, Norway
- Department of Psychiatry, University of Oxford, Oxford, United Kingdom
- LREN, Centre for Research in Neurosciences, Department of Clinical Neurosciences, Lausanne University Hospital, University of Lausanne, Lausanne, Switzerland
| | - Dennis van der Meer
- Norwegian Centre for Mental Disorder Research (NORMENT), Division of Mental Health and Addiction, Oslo University Hospital, University of Oslo, Oslo, Norway
- Faculty of Health, Medicine and Life Sciences, Maastricht University, Maastricht, Netherlands
| | - Dani Beck
- Norwegian Centre for Mental Disorder Research (NORMENT), Division of Mental Health and Addiction, Oslo University Hospital, University of Oslo, Oslo, Norway
- Department of Psychiatric Research, Diakonhjemmet Hospital, Oslo, Norway
- Department of Psychology, University of Oslo, Oslo, Norway
| | - Eli Eikefjord
- Department of Health and Functioning, Western Norway University of Applied Sciences, Bergen, Norway
- Mohn Medical Imaging and Visualization Center (MMIV), Bergen, Norway
| | - Arvid Lundervold
- Department of Health and Functioning, Western Norway University of Applied Sciences, Bergen, Norway
- Mohn Medical Imaging and Visualization Center (MMIV), Bergen, Norway
- Department of Radiology, Haukeland University Hospital, Bergen, Norway
- Department of Biomedicine, University of Bergen, Bergen, Norway
| | - Ole A. Andreassen
- Norwegian Centre for Mental Disorder Research (NORMENT), Division of Mental Health and Addiction, Oslo University Hospital, University of Oslo, Oslo, Norway
- KG Jebsen Centre for Neurodevelopmental Disorders, University of Oslo, Oslo, Norway
| | - Lars T. Westlye
- Norwegian Centre for Mental Disorder Research (NORMENT), Division of Mental Health and Addiction, Oslo University Hospital, University of Oslo, Oslo, Norway
- Department of Psychology, University of Oslo, Oslo, Norway
- KG Jebsen Centre for Neurodevelopmental Disorders, University of Oslo, Oslo, Norway
| | - Ivan I. Maximov
- Department of Health and Functioning, Western Norway University of Applied Sciences, Bergen, Norway
- Norwegian Centre for Mental Disorder Research (NORMENT), Division of Mental Health and Addiction, Oslo University Hospital, University of Oslo, Oslo, Norway
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35
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Levitt JJ, Zhang F, Vangel M, Nestor PG, Rathi Y, Cetin-Karayumak S, Kubicki M, Coleman MJ, Lewandowski KE, Holt DJ, Keshavan M, Bouix S, Öngür D, Breier A, Shenton ME, O'Donnell LJ. The organization of frontostriatal brain wiring in non-affective early psychosis compared with healthy subjects using a novel diffusion imaging fiber cluster analysis. Mol Psychiatry 2023; 28:2301-2311. [PMID: 37173451 DOI: 10.1038/s41380-023-02031-0] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/13/2022] [Revised: 02/13/2023] [Accepted: 03/08/2023] [Indexed: 05/15/2023]
Abstract
BACKGROUND Alterations in brain connectivity may underlie neuropsychiatric conditions such as schizophrenia. We here assessed the degree of convergence of frontostriatal fiber projections in 56 young adult healthy controls (HCs) and 108 matched Early Psychosis-Non-Affective patients (EP-NAs) using our novel fiber cluster analysis of whole brain diffusion magnetic resonance imaging tractography. METHODS Using whole brain tractography and our fiber clustering methodology on harmonized diffusion magnetic resonance imaging data from the Human Connectome Project for Early Psychosis we identified 17 white matter fiber clusters that connect frontal cortex (FCtx) and caudate (Cd) per hemisphere in each group. To quantify the degree of convergence and, hence, topographical relationship of these fiber clusters, we measured the inter-cluster mean distances between the endpoints of the fiber clusters at the level of the FCtx and of the Cd, respectively. RESULTS We found (1) in both groups, bilaterally, a non-linear relationship, yielding convex curves, between FCtx and Cd distances for FCtx-Cd connecting fiber clusters, driven by a cluster projecting from inferior frontal gyrus; however, in the right hemisphere, the convex curve was more flattened in EP-NAs; (2) that cluster pairs in the right (p = 0.03), but not left (p = 0.13), hemisphere were significantly more convergent in HCs vs EP-NAs; (3) in both groups, bilaterally, similar clusters projected significantly convergently to the Cd; and, (4) a significant group by fiber cluster pair interaction for 2 right hemisphere fiber clusters (numbers 5, 11; p = .00023; p = .00023) originating in selective PFC subregions. CONCLUSIONS In both groups, we found the FCtx-Cd wiring pattern deviated from a strictly topographic relationship and that similar clusters projected significantly more convergently to the Cd. Interestingly, we also found a significantly more convergent pattern of connectivity in HCs in the right hemisphere and that 2 clusters from PFC subregions in the right hemisphere significantly differed in their pattern of connectivity between groups.
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Affiliation(s)
- J J Levitt
- Department of Psychiatry, VA Boston Healthcare System, Brockton Division, Brockton, MA, 02301, USA.
- Harvard Medical School, Boston, MA, 02115, USA.
- Psychiatry Neuroimaging Laboratory, Department of Psychiatry, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, 02115, USA.
| | - F Zhang
- Department of Radiology, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, 02115, USA
| | - M Vangel
- Department of Psychiatry, Massachusetts General Hospital, Harvard Medical School, Boston, MA, 02114, USA
| | - P G Nestor
- Department of Psychiatry, VA Boston Healthcare System, Brockton Division, Brockton, MA, 02301, USA
- Harvard Medical School, Boston, MA, 02115, USA
- Department of Psychology, University of Massachusetts, Boston, MA, 02125, USA
| | - Y Rathi
- Psychiatry Neuroimaging Laboratory, Department of Psychiatry, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, 02115, USA
- Department of Psychiatry, Massachusetts General Hospital, Harvard Medical School, Boston, MA, 02114, USA
| | - S Cetin-Karayumak
- Psychiatry Neuroimaging Laboratory, Department of Psychiatry, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, 02115, USA
| | - M Kubicki
- Psychiatry Neuroimaging Laboratory, Department of Psychiatry, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, 02115, USA
- Department of Radiology, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, 02115, USA
- Department of Psychiatry, Massachusetts General Hospital, Harvard Medical School, Boston, MA, 02114, USA
| | - M J Coleman
- Psychiatry Neuroimaging Laboratory, Department of Psychiatry, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, 02115, USA
| | - K E Lewandowski
- McLean Hospital, Harvard Medical School, Belmont, MA, 02478, USA
| | - D J Holt
- Department of Psychiatry, Massachusetts General Hospital, Harvard Medical School, Boston, MA, 02114, USA
| | - M Keshavan
- Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, MA, 02215, USA
| | - S Bouix
- Psychiatry Neuroimaging Laboratory, Department of Psychiatry, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, 02115, USA
- Department of Software Engineering and Information Technology, École de technologie supérieure, Université du Québec, Montréal, QC, H3C 1K3, Canada
| | - D Öngür
- McLean Hospital, Harvard Medical School, Belmont, MA, 02478, USA
| | - A Breier
- Indiana University School of Medicine, Indianapolis, IN, 46202, USA
| | - M E Shenton
- Psychiatry Neuroimaging Laboratory, Department of Psychiatry, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, 02115, USA
- Department of Radiology, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, 02115, USA
- Department of Psychiatry, Massachusetts General Hospital, Harvard Medical School, Boston, MA, 02114, USA
| | - L J O'Donnell
- Department of Radiology, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, 02115, USA
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Langhein M, Lyall AE, Steinmann S, Seitz-Holland J, Nägele FL, Cetin-Karayumak S, Zhang F, Rauh J, Mußmann M, Billah T, Makris N, Pasternak O, O’Donnell LJ, Rathi Y, Leicht G, Kubicki M, Shenton ME, Mulert C. The decoupling of structural and functional connectivity of auditory networks in individuals at clinical high-risk for psychosis. World J Biol Psychiatry 2023; 24:387-399. [PMID: 36083108 PMCID: PMC10399965 DOI: 10.1080/15622975.2022.2112974] [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: 04/20/2022] [Revised: 08/09/2022] [Accepted: 08/10/2022] [Indexed: 10/14/2022]
Abstract
OBJECTIVES Disrupted auditory networks play an important role in the pathophysiology of psychosis, with abnormalities already observed in individuals at clinical high-risk for psychosis (CHR). Here, we examine structural and functional connectivity of an auditory network in CHR utilising state-of-the-art electroencephalography and diffusion imaging techniques. METHODS Twenty-six CHR subjects and 13 healthy controls (HC) underwent diffusion MRI and electroencephalography while performing an auditory task. We investigated structural connectivity, measured as fractional anisotropy in the Arcuate Fasciculus (AF), Cingulum Bundle, and Superior Longitudinal Fasciculus-II. Gamma-band lagged-phase synchronisation, a functional connectivity measure, was calculated between cortical regions connected by these tracts. RESULTS CHR subjects showed significantly higher structural connectivity in the right AF than HC (p < .001). Although non-significant, functional connectivity between cortical areas connected by the AF was lower in CHR than HC (p = .078). Structural and functional connectivity were correlated in HC (p = .056) but not in CHR (p = .29). CONCLUSIONS We observe significant differences in structural connectivity of the AF, without a concomitant significant change in functional connectivity in CHR subjects. This may suggest that the CHR state is characterised by a decoupling of structural and functional connectivity, possibly due to abnormal white matter maturation.
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Affiliation(s)
- Mina Langhein
- Psychiatry Neuroimaging Branch, Department of Psychiatry and Psychotherapy, University Medical Center Hamburg-Eppendorf, Hamburg, Germany
- Psychiatry Neuroimaging Laboratory, Department of Psychiatry, Brigham and Women’s Hospital, Harvard Medical School, Boston, MA, USA
| | - Amanda E. Lyall
- Psychiatry Neuroimaging Laboratory, Department of Psychiatry, Brigham and Women’s Hospital, Harvard Medical School, Boston, MA, USA
- Department of Psychiatry, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
| | - Saskia Steinmann
- Psychiatry Neuroimaging Branch, Department of Psychiatry and Psychotherapy, University Medical Center Hamburg-Eppendorf, Hamburg, Germany
- Psychiatry Neuroimaging Laboratory, Department of Psychiatry, Brigham and Women’s Hospital, Harvard Medical School, Boston, MA, USA
| | - Johanna Seitz-Holland
- Psychiatry Neuroimaging Laboratory, Department of Psychiatry, Brigham and Women’s Hospital, Harvard Medical School, Boston, MA, USA
| | - Felix L. Nägele
- Psychiatry Neuroimaging Branch, Department of Psychiatry and Psychotherapy, University Medical Center Hamburg-Eppendorf, Hamburg, Germany
- Psychiatry Neuroimaging Laboratory, Department of Psychiatry, Brigham and Women’s Hospital, Harvard Medical School, Boston, MA, USA
| | - Suheyla Cetin-Karayumak
- Psychiatry Neuroimaging Laboratory, Department of Psychiatry, Brigham and Women’s Hospital, Harvard Medical School, Boston, MA, USA
| | - Fan Zhang
- Department of Radiology, Brigham and Women’s Hospital, Harvard Medical School, Boston, MA, USA
| | - Jonas Rauh
- Psychiatry Neuroimaging Branch, Department of Psychiatry and Psychotherapy, University Medical Center Hamburg-Eppendorf, Hamburg, Germany
| | - Marius Mußmann
- Psychiatry Neuroimaging Branch, Department of Psychiatry and Psychotherapy, University Medical Center Hamburg-Eppendorf, Hamburg, Germany
| | - Tashrif Billah
- Psychiatry Neuroimaging Laboratory, Department of Psychiatry, Brigham and Women’s Hospital, Harvard Medical School, Boston, MA, USA
| | - Nikos Makris
- Psychiatry Neuroimaging Laboratory, Department of Psychiatry, Brigham and Women’s Hospital, Harvard Medical School, Boston, MA, USA
- Department of Psychiatry, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
| | - Ofer Pasternak
- Psychiatry Neuroimaging Laboratory, Department of Psychiatry, Brigham and Women’s Hospital, Harvard Medical School, Boston, MA, USA
- Department of Radiology, Brigham and Women’s Hospital, Harvard Medical School, Boston, MA, USA
| | - Lauren J O’Donnell
- Department of Radiology, Brigham and Women’s Hospital, Harvard Medical School, Boston, MA, USA
| | - Yogesh Rathi
- Psychiatry Neuroimaging Laboratory, Department of Psychiatry, Brigham and Women’s Hospital, Harvard Medical School, Boston, MA, USA
- Department of Radiology, Brigham and Women’s Hospital, Harvard Medical School, Boston, MA, USA
| | - Gregor Leicht
- Psychiatry Neuroimaging Branch, Department of Psychiatry and Psychotherapy, University Medical Center Hamburg-Eppendorf, Hamburg, Germany
| | - Marek Kubicki
- Psychiatry Neuroimaging Laboratory, Department of Psychiatry, Brigham and Women’s Hospital, Harvard Medical School, Boston, MA, USA
- Department of Psychiatry, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
| | - Martha E. Shenton
- Psychiatry Neuroimaging Laboratory, Department of Psychiatry, Brigham and Women’s Hospital, Harvard Medical School, Boston, MA, USA
- Department of Psychiatry, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
- Department of Radiology, Brigham and Women’s Hospital, Harvard Medical School, Boston, MA, USA
| | - Christoph Mulert
- Psychiatry Neuroimaging Branch, Department of Psychiatry and Psychotherapy, University Medical Center Hamburg-Eppendorf, Hamburg, Germany
- Centre for Psychiatry, Justus-Liebig-University, Giessen, Germany
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37
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Cetin-Karayumak S, Zhang F, Billah T, Zekelman L, Makris N, Pieper S, O’Donnell LJ, Rathi Y. Harmonized diffusion MRI data and white matter measures from the Adolescent Brain Cognitive Development Study. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.04.04.535587. [PMID: 37066186 PMCID: PMC10104063 DOI: 10.1101/2023.04.04.535587] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 04/18/2023]
Abstract
The Adolescent Brain Cognitive Development (ABCD) study has collected data from over 10,000 children across 21 sites, providing valuable insights into adolescent brain development. However, site-specific scanner variability has made it challenging to use diffusion MRI (dMRI) data from this study. To address this, a database of harmonized and processed ABCD dMRI data has been created, comprising quality-controlled imaging data from 9345 subjects. This resource required significant computational effort, taking ~50,000 CPU hours to harmonize the data, perform white matter parcellation, and run whole brain tractography. The database includes harmonized dMRI data, 800 white matter clusters, 73 anatomically labeled white matter tracts both in full-resolution (for analysis) and low-resolution (for visualization), and 804 different dMRI-derived measures per subject. It is available via the NIMH Data Archive and offers tremendous potential for scientific discoveries in structural connectivity studies of neurodevelopment in children and adolescents. Additionally, several post-harmonization experiments were conducted to demonstrate the success of the harmonization process on the ABCD dataset.
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Affiliation(s)
- Suheyla Cetin-Karayumak
- Department of Psychiatry, Brigham and Women’s Hospital, Harvard Medical School, Boston, Massachusetts, USA
| | - Fan Zhang
- Department of Radiology, Brigham and Women’s Hospital, Harvard Medical School, Boston, Massachusetts, USA
| | - Tashrif Billah
- Department of Psychiatry, Brigham and Women’s Hospital, Harvard Medical School, Boston, Massachusetts, USA
| | - Leo Zekelman
- Department of Neurosurgery, Brigham and Women’s Hospital, Harvard Medical School, Boston, Massachusetts, USA
- Program in Speech and Hearing Bioscience and Technology, Division of Medical Sciences, Harvard University, Boston, Massachusetts, USA
| | - Nikos Makris
- Department of Psychiatry, Brigham and Women’s Hospital, Harvard Medical School, Boston, Massachusetts, USA
- Department of Psychiatry, Massachusetts General Hospital, Harvard Medical School, Boston, Massachusetts, USA
| | | | - Lauren J. O’Donnell
- Department of Radiology, Brigham and Women’s Hospital, Harvard Medical School, Boston, Massachusetts, USA
| | - Yogesh Rathi
- Department of Psychiatry, Brigham and Women’s Hospital, Harvard Medical School, Boston, Massachusetts, USA
- Department of Radiology, Brigham and Women’s Hospital, Harvard Medical School, Boston, Massachusetts, USA
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38
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Cetin-Karayumak S, Lyall AE, Di Biase MA, Seitz-Holland J, Zhang F, Kelly S, Elad D, Pearlson G, Tamminga CA, Sweeney JA, Clementz BA, Schretlen D, Stegmayer K, Walther S, Lee J, Crow T, James A, Voineskos A, Buchanan RW, Szeszko PR, Malhotra AK, Keshavan M, Shenton ME, Rathi Y, Pasternak O, Kubicki M. Characterization of the extracellular free water signal in schizophrenia using multi-site diffusion MRI harmonization. Mol Psychiatry 2023; 28:2030-2038. [PMID: 37095352 PMCID: PMC11146151 DOI: 10.1038/s41380-023-02068-1] [Citation(s) in RCA: 11] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/09/2022] [Revised: 03/06/2023] [Accepted: 04/05/2023] [Indexed: 04/26/2023]
Abstract
Studies applying Free Water Imaging have consistently reported significant global increases in extracellular free water (FW) in populations of individuals with early psychosis. However, these published studies focused on homogenous clinical participant groups (e.g., only first episode or chronic), thereby limiting our understanding of the time course of free water elevations across illness stages. Moreover, the relationship between FW and duration of illness has yet to be directly tested. Leveraging our multi-site diffusion magnetic resonance imaging(dMRI) harmonization approach, we analyzed dMRI scans collected by 12 international sites from 441 healthy controls and 434 individuals diagnosed with schizophrenia-spectrum disorders at different illness stages and ages (15-58 years). We characterized the pattern of age-related FW changes by assessing whole brain white matter in individuals with schizophrenia and healthy controls. In individuals with schizophrenia, average whole brain FW was higher than in controls across all ages, with the greatest FW values observed from 15 to 23 years (effect size range = [0.70-0.87]). Following this peak, FW exhibited a monotonic decrease until reaching a minima at the age of 39 years. After 39 years, an attenuated monotonic increase in FW was observed, but with markedly smaller effect sizes when compared to younger patients (effect size range = [0.32-0.43]). Importantly, FW was found to be negatively associated with duration of illness in schizophrenia (p = 0.006), independent of the effects of other clinical and demographic data. In summary, our study finds in a large, age-diverse sample that participants with schizophrenia with a shorter duration of illness showed higher FW values compared to participants with more prolonged illness. Our findings provide further evidence that elevations in the FW are present in individuals with schizophrenia, with the greatest differences in the FW being observed in those at the early stages of the disorder, which might suggest acute extracellular processes.
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Affiliation(s)
- Suheyla Cetin-Karayumak
- Department of Psychiatry, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA.
| | - Amanda E Lyall
- Department of Psychiatry, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
- Department of Psychiatry, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
| | - Maria A Di Biase
- Department of Psychiatry, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
- Melbourne Neuropsychiatry Centre, Department of Psychiatry, The University of Melbourne and Melbourne Health, Carlton South, VIC, Australia
| | - Johanna Seitz-Holland
- Department of Psychiatry, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
- Department of Psychiatry, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
| | - Fan Zhang
- Department of Radiology, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
| | - Sinead Kelly
- Department of Psychiatry, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
- Department of Psychiatry, Beth Israel Deaconess Medical Centre, Harvard Medical School, Boston, MA, USA
| | - Doron Elad
- Ruth and Bruce Rappaport Faculty of Medicine, Technion-Israel Institute of Technology, Haifa, Israel
| | | | - Carol A Tamminga
- Department of Psychiatry, UT Southwestern Medical Center, Dallas, TX, USA
| | - John A Sweeney
- Department of Psychiatry and Behavioral Neuroscience, University of Cincinnati, Cincinnati, OH, USA
| | - Brett A Clementz
- Departments of Psychology and Neuroscience, Bio-Imaging Research Center, University of Georgia, Athens, GA, USA
| | - David Schretlen
- Department of Psychiatry and Behavioral Sciences, Morgan Department of Radiology and Radiological Science, Johns Hopkins Medical Institutions, Baltimore, MD, USA
| | - Katharina Stegmayer
- University Hospital of Psychiatry and Psychotherapy, University of Bern, Bern, Switzerland
| | - Sebastian Walther
- University Hospital of Psychiatry and Psychotherapy, University of Bern, Bern, Switzerland
| | - Jungsun Lee
- Department of Psychiatry, University of Ulsan College of Medicine, Asan Medical Center, Seoul, Korea
| | - Tim Crow
- Department of Psychiatry, SANE POWIC, Warneford Hospital, University of Oxford, Oxford, UK
| | - Anthony James
- Department of Psychiatry, SANE POWIC, Warneford Hospital, University of Oxford, Oxford, UK
| | | | - Robert W Buchanan
- Maryland Psychiatric Research Center, University of Maryland School of Medicine, Baltimore, MD, USA
| | - Philip R Szeszko
- Departments of Psychiatry and Neuroscience, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- Mental Illness Research, Education, and Clinical Center, James J. Peters Veterans Affairs Medical Center, Bronx, NY, USA
| | - Anil K Malhotra
- The Feinstein Institutes for Medical Research and Zucker Hillside Hospital, Glen Oaks, NY, USA
| | - Matcheri Keshavan
- Department of Psychiatry, Beth Israel Deaconess Medical Centre, Harvard Medical School, Boston, MA, USA
| | - Martha E Shenton
- Department of Psychiatry, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
- Department of Psychiatry, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
- Department of Radiology, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
| | - Yogesh Rathi
- Department of Psychiatry, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
- Department of Psychiatry, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
- Department of Radiology, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
| | - Ofer Pasternak
- Department of Psychiatry, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
- Department of Psychiatry, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
- Department of Radiology, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
| | - Marek Kubicki
- Department of Psychiatry, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
- Department of Psychiatry, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
- Department of Radiology, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
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39
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Su W, Yuan A, Tang Y, Xu L, Wei Y, Wang Y, Li Z, Cui H, Qian Z, Tang X, Hu Y, Zhang T, Feng J, Li Z, Zhang J, Wang J. Effects of polygenic risk of schizophrenia on interhemispheric callosal white matter integrity and frontotemporal functional connectivity in first-episode schizophrenia. Psychol Med 2023; 53:2868-2877. [PMID: 34991756 DOI: 10.1017/s0033291721004840] [Citation(s) in RCA: 2] [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] [Indexed: 12/26/2022]
Abstract
BACKGROUND Schizophrenia is a severely debilitating psychiatric disorder with high heritability and polygenic architecture. A higher polygenic risk score for schizophrenia (SzPRS) has been associated with smaller gray matter volume, lower activation, and decreased functional connectivity (FC). However, the effect of polygenic inheritance on the brain white matter microstructure has only been sparsely reported. METHODS Eighty-four patients with first-episode schizophrenia (FES) patients and ninety-three healthy controls (HC) with genetics, diffusion tensor imaging (DTI), and resting-state functional magnetic resonance imaging (rs-fMRI) data were included in our study. We investigated impaired white matter integrity as measured by fractional anisotropy (FA) in the FES group, further examined the effect of SzPRS on white matter FA and FC in the regions connected by SzPRS-related white matter tracts. RESULTS Decreased FA was observed in FES in many commonly identified regions. Among these regions, we observed that in the FES group, but not the HC group, SzPRS was negatively associated with the mean FA in the genu and body of corpus callosum, right anterior corona radiata, and right superior corona radiata. Higher SzPRS was also associated with lower FCs between the left inferior frontal gyrus (IFG)-left inferior temporal gyrus (ITG), right IFG-left ITG, right IFG-left middle frontal gyrus (MFG), and right IFG-right MFG in the FES group. CONCLUSION Higher polygenic risks are linked with disrupted white matter integrity and FC in patients with schizophrenia. These correlations are strongly driven by the interhemispheric callosal fibers and the connections between frontotemporal regions.
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Affiliation(s)
- Wenjun Su
- Shanghai Mental Health Center, Shanghai Jiao Tong University School of Medicine, Shanghai 200030, China
| | - Aihua Yuan
- Shanghai Mental Health Center, Shanghai Jiao Tong University School of Medicine, Shanghai 200030, China
| | - Yingying Tang
- Shanghai Mental Health Center, Shanghai Jiao Tong University School of Medicine, Shanghai 200030, China
| | - Lihua Xu
- Shanghai Mental Health Center, Shanghai Jiao Tong University School of Medicine, Shanghai 200030, China
| | - Yanyan Wei
- Shanghai Mental Health Center, Shanghai Jiao Tong University School of Medicine, Shanghai 200030, China
| | - Yingchan Wang
- Shanghai Mental Health Center, Shanghai Jiao Tong University School of Medicine, Shanghai 200030, China
| | - Zhixing Li
- Shanghai Mental Health Center, Shanghai Jiao Tong University School of Medicine, Shanghai 200030, China
| | - Huiru Cui
- Shanghai Mental Health Center, Shanghai Jiao Tong University School of Medicine, Shanghai 200030, China
| | - Zhenying Qian
- Shanghai Mental Health Center, Shanghai Jiao Tong University School of Medicine, Shanghai 200030, China
| | - Xiaochen Tang
- Shanghai Mental Health Center, Shanghai Jiao Tong University School of Medicine, Shanghai 200030, China
| | - Yegang Hu
- Shanghai Mental Health Center, Shanghai Jiao Tong University School of Medicine, Shanghai 200030, China
| | - Tianhong Zhang
- Shanghai Mental Health Center, Shanghai Jiao Tong University School of Medicine, Shanghai 200030, China
| | - Jianfeng Feng
- Institute of Science and Technology for Brain-Inspired Intelligence, Fudan University, Shanghai 200433, China
| | - Zhiqiang Li
- Affiliated Hospital of Qingdao University & Biomedical Sciences Institute of Qingdao University, Qingdao University, Qingdao 266000, China
- Bio-X Institutes, Key Laboratory for the Genetics of Developmental and Neuropsychiatric Disorders (Ministry of Education), Collaborative Innovation Centre for Brain Science, Shanghai Jiao Tong University, Shanghai 200240, China
| | - Jie Zhang
- Institute of Science and Technology for Brain-Inspired Intelligence, Fudan University, Shanghai 200433, China
| | - Jijun Wang
- Shanghai Mental Health Center, Shanghai Jiao Tong University School of Medicine, Shanghai 200030, China
- CAS Center for Excellence in Brain Science and Intelligence Technology, Chinese Academy of Science, Shanghai 200031, China
- Institute of Psychology and Behavioral Science, Shanghai Jiao Tong University, Shanghai 200240, China
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40
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Vid Prkačin M, Banovac I, Petanjek Z, Hladnik A. Cortical interneurons in schizophrenia - cause or effect? Croat Med J 2023; 64:110-122. [PMID: 37131313 PMCID: PMC10183954 DOI: 10.3325/cmj.2023.64.110] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/17/2022] [Accepted: 02/15/2023] [Indexed: 12/09/2024] Open
Abstract
GABAergic cortical interneurons are important components of cortical microcircuits. Their alterations are associated with a number of neurological and psychiatric disorders, and are thought to be especially important in the pathogenesis of schizophrenia. Here, we reviewed neuroanatomical and histological studies that analyzed different populations of cortical interneurons in postmortem human tissue from patients with schizophrenia and adequately matched controls. The data strongly suggests that in schizophrenia only selective interneuron populations are affected, with alterations of somatostatin and parvalbumin neurons being the most convincing. The most prominent changes are found in the prefrontal cortex, which is consistent with the impairment of higher cognitive functions characteristic of schizophrenia. In contrast, calretinin neurons, the most numerous interneuron population in primates, seem to be largely unaffected. The selective alterations of cortical interneurons are in line with the neurodevelopmental model and the multiple-hit hypothesis of schizophrenia. Nevertheless, a large number of data on interneurons in schizophrenia is still inconclusive, with different studies yielding opposing findings. Furthermore, no studies found a clear link between interneuron alterations and clinical outcomes. Future research should focus on the causes of changes in the cortical microcircuitry in order to identify potential therapeutic targets.
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Affiliation(s)
| | - Ivan Banovac
- Ivan Banovac, Department of Anatomy and Clinical Anatomy, University of Zagreb School of Medicine, Šalata 11, 10 000 Zagreb, Croatia,
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41
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Tang B, Zhang W, Liu J, Deng S, Hu N, Li S, Zhao Y, Liu N, Zeng J, Cao H, Sweeney JA, Gong Q, Gu S, Lui S. Altered controllability of white matter networks and related brain function changes in first-episode drug-naive schizophrenia. Cereb Cortex 2023; 33:1527-1535. [PMID: 36790361 DOI: 10.1093/cercor/bhac421] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/24/2022] [Revised: 09/25/2022] [Accepted: 09/26/2022] [Indexed: 11/06/2022] Open
Abstract
Understanding how structural connectivity alterations affect aberrant dynamic function using network control theory will provide new mechanistic insights into the pathophysiology of schizophrenia. The study included 140 drug-naive schizophrenia patients and 119 healthy controls (HCs). The average controllability (AC) quantifying capacity of brain regions/networks to shift the system into easy-to-reach states was calculated based on white matter connectivity and was compared between patients and HCs as well as functional network topological and dynamic properties. The correlation analysis between AC and duration of untreated psychosis (DUP) were conducted to characterize the controllability progression pattern without treatment effects. Relative to HCs, patients exhibited reduced AC in multiple nodes, mainly distributed in default mode network (DMN), visual network (VN), and subcortical regions, and increased AC in somatomotor network. These networks also had impaired functional topology and increased temporal variability in dynamic functional connectivity analysis. Longer DUP was related to greater reductions of AC in VN and DMN. The current study highlighted potential structural substrates underlying altered functional dynamics in schizophrenia, providing a novel understanding of the relationship of anatomic and functional network alterations.
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Affiliation(s)
- Biqiu Tang
- Huaxi MR Research Center (HMRRC), Functional and Molecular Imaging Key Laboratory of Sichuan Province, Department of Radiology, West China Hospital of Sichuan University, No. 37 Guoxue Xiang, Chengdu 610041, China.,Research Unit of Psychoradiology, Chinese Academy of Medical Sciences, No. 37 Guoxue Xiang, Chengdu 610041, China
| | - Wenjing Zhang
- Huaxi MR Research Center (HMRRC), Functional and Molecular Imaging Key Laboratory of Sichuan Province, Department of Radiology, West China Hospital of Sichuan University, No. 37 Guoxue Xiang, Chengdu 610041, China.,Research Unit of Psychoradiology, Chinese Academy of Medical Sciences, No. 37 Guoxue Xiang, Chengdu 610041, China
| | - Jiang Liu
- School of Computer Science and Engineering, University of Electronic Science and Technology of China, No. 2006 Xiyuan Avenue, Chengdu 611731, China
| | - Shikuang Deng
- School of Computer Science and Engineering, University of Electronic Science and Technology of China, No. 2006 Xiyuan Avenue, Chengdu 611731, China
| | - Na Hu
- Huaxi MR Research Center (HMRRC), Functional and Molecular Imaging Key Laboratory of Sichuan Province, Department of Radiology, West China Hospital of Sichuan University, No. 37 Guoxue Xiang, Chengdu 610041, China.,Research Unit of Psychoradiology, Chinese Academy of Medical Sciences, No. 37 Guoxue Xiang, Chengdu 610041, China
| | - Siyi Li
- Huaxi MR Research Center (HMRRC), Functional and Molecular Imaging Key Laboratory of Sichuan Province, Department of Radiology, West China Hospital of Sichuan University, No. 37 Guoxue Xiang, Chengdu 610041, China.,Research Unit of Psychoradiology, Chinese Academy of Medical Sciences, No. 37 Guoxue Xiang, Chengdu 610041, China
| | - Youjin Zhao
- Huaxi MR Research Center (HMRRC), Functional and Molecular Imaging Key Laboratory of Sichuan Province, Department of Radiology, West China Hospital of Sichuan University, No. 37 Guoxue Xiang, Chengdu 610041, China.,Research Unit of Psychoradiology, Chinese Academy of Medical Sciences, No. 37 Guoxue Xiang, Chengdu 610041, China
| | - Nian Liu
- Department of Radiology, Affiliated Hospital of North Sichuan Medical College, No 1 Maoyuan South Road, Shunqing District, Nanchong 637000, China
| | - Jiaxin Zeng
- Huaxi MR Research Center (HMRRC), Functional and Molecular Imaging Key Laboratory of Sichuan Province, Department of Radiology, West China Hospital of Sichuan University, No. 37 Guoxue Xiang, Chengdu 610041, China.,Research Unit of Psychoradiology, Chinese Academy of Medical Sciences, No. 37 Guoxue Xiang, Chengdu 610041, China
| | - Hengyi Cao
- Huaxi MR Research Center (HMRRC), Functional and Molecular Imaging Key Laboratory of Sichuan Province, Department of Radiology, West China Hospital of Sichuan University, No. 37 Guoxue Xiang, Chengdu 610041, China.,Center for Psychiatric Neuroscience, Feinstein Institute for Medical Research, 350 Community Drive, Manhasset, NY 11030, United States.,Division of Psychiatry Research, Zucker Hillside Hospital, 75-59 263rd Street, Glen Oaks, NY 11004, United States
| | - John A Sweeney
- Huaxi MR Research Center (HMRRC), Functional and Molecular Imaging Key Laboratory of Sichuan Province, Department of Radiology, West China Hospital of Sichuan University, No. 37 Guoxue Xiang, Chengdu 610041, China.,Department of Psychiatry and Behavioral Neuroscience, University of Cincinnati College of Medicine, 260 Stetson Street, Cincinnati, OH 45219, United States
| | - Qiyong Gong
- Huaxi MR Research Center (HMRRC), Functional and Molecular Imaging Key Laboratory of Sichuan Province, Department of Radiology, West China Hospital of Sichuan University, No. 37 Guoxue Xiang, Chengdu 610041, China.,Research Unit of Psychoradiology, Chinese Academy of Medical Sciences, No. 37 Guoxue Xiang, Chengdu 610041, China
| | - Shi Gu
- School of Computer Science and Engineering, University of Electronic Science and Technology of China, No. 2006 Xiyuan Avenue, Chengdu 611731, China
| | - Su Lui
- Huaxi MR Research Center (HMRRC), Functional and Molecular Imaging Key Laboratory of Sichuan Province, Department of Radiology, West China Hospital of Sichuan University, No. 37 Guoxue Xiang, Chengdu 610041, China.,Research Unit of Psychoradiology, Chinese Academy of Medical Sciences, No. 37 Guoxue Xiang, Chengdu 610041, China
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Vinogradov S, Chafee MV, Lee E, Morishita H. Psychosis spectrum illnesses as disorders of prefrontal critical period plasticity. Neuropsychopharmacology 2023; 48:168-185. [PMID: 36180784 PMCID: PMC9700720 DOI: 10.1038/s41386-022-01451-w] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/29/2022] [Revised: 08/17/2022] [Accepted: 08/21/2022] [Indexed: 01/05/2023]
Abstract
Emerging research on neuroplasticity processes in psychosis spectrum illnesses-from the synaptic to the macrocircuit levels-fill key gaps in our models of pathophysiology and open up important treatment considerations. In this selective narrative review, we focus on three themes, emphasizing alterations in spike-timing dependent and Hebbian plasticity that occur during adolescence, the critical period for prefrontal system development: (1) Experience-dependent dysplasticity in psychosis emerges from activity decorrelation within neuronal ensembles. (2) Plasticity processes operate bidirectionally: deleterious environmental and experiential inputs shape microcircuits. (3) Dysregulated plasticity processes interact across levels of scale and time and include compensatory mechanisms that have pathogenic importance. We present evidence that-given the centrality of progressive dysplastic changes, especially in prefrontal cortex-pharmacologic or neuromodulatory interventions will need to be supplemented by corrective learning experiences for the brain if we are to help people living with these illnesses to fully thrive.
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Affiliation(s)
- Sophia Vinogradov
- Department of Psychiatry & Behavioral Science, University of Minnesota Medical School, Minneapolis, MN, USA.
| | - Matthew V Chafee
- Department of Neuroscience, University of Minnesota Medical School, Minneapolis, MN, USA
| | - Erik Lee
- Masonic Institute for the Developing Brain, University of Minnesota Medical School, Minneapolis, MN, USA
- University of Minnesota Informatics Institute, University of Minnesota, Minneapolis, MN, USA
| | - Hirofumi Morishita
- Department of Psychiatry, Neuroscience, & Ophthalmology, Icahn School of Medicine at Mount Sinai, New York, NY, USA
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43
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Manic KS, Rajinikanth V, Al-Bimani AS, Taniar D, Kadry S. Framework to Detect Schizophrenia in Brain MRI Slices with Mayfly Algorithm-Selected Deep and Handcrafted Features. SENSORS (BASEL, SWITZERLAND) 2022; 23:280. [PMID: 36616876 PMCID: PMC9823879 DOI: 10.3390/s23010280] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 10/21/2022] [Revised: 12/21/2022] [Accepted: 12/24/2022] [Indexed: 06/17/2023]
Abstract
Brain abnormality causes severe human problems, and thorough screening is necessary to identify the disease. In clinics, bio-image-supported brain abnormality screening is employed mainly because of its investigative accuracy compared with bio-signal (EEG)-based practice. This research aims to develop a reliable disease screening framework for the automatic identification of schizophrenia (SCZ) conditions from brain MRI slices. This scheme consists following phases: (i) MRI slices collection and pre-processing, (ii) implementation of VGG16 to extract deep features (DF), (iii) collection of handcrafted features (HF), (iv) mayfly algorithm-supported optimal feature selection, (v) serial feature concatenation, and (vi) binary classifier execution and validation. The performance of the proposed scheme was independently tested with DF, HF, and concatenated features (DF+HF), and the achieved outcome of this study verifies that the schizophrenia screening accuracy with DF+HF is superior compared with other methods. During this work, 40 patients’ brain MRI images (20 controlled and 20 SCZ class) were considered for the investigation, and the following accuracies were achieved: DF provided >91%, HF obtained >85%, and DF+HF achieved >95%. Therefore, this framework is clinically significant, and in the future, it can be used to inspect actual patients’ brain MRI slices.
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Affiliation(s)
- K. Suresh Manic
- National University of Science and Technology, Muscat P.O. Box 112, Oman
| | - Venkatesan Rajinikanth
- Department of Computer Science and Engineering, Division of Research and Innovation, Saveetha School of Engineering, Saveetha Institute of Medical and Technical Sciences, Chennai 602105, India
| | - Ali Saud Al-Bimani
- National University of Science and Technology, Muscat P.O. Box 112, Oman
| | - David Taniar
- Faculty of Information Technology, Monash University, Wellington Rd, Clayton, VIC 3800, Australia
| | - Seifedine Kadry
- Department of Applied Data Science, Noroff University College, 4612 Kristiansand, Norway
- Artificial Intelligence Research Center (AIRC), Ajman University, Ajman P.O. Box 346, United Arab Emirates
- Department of Electrical and Computer Engineering, Lebanese American University, Byblos P.O. Box 36, Lebanon
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44
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Zhao Q, Cao H, Zhang W, Li S, Xiao Y, Tamminga CA, Keshavan MS, Pearlson GD, Clementz BA, Gershon ES, Hill SK, Keedy SK, Ivleva EI, Lencer R, Sweeney JA, Gong Q, Lui S. A subtype of institutionalized patients with schizophrenia characterized by pronounced subcortical and cognitive deficits. Neuropsychopharmacology 2022; 47:2024-2032. [PMID: 35260788 PMCID: PMC9556672 DOI: 10.1038/s41386-022-01300-w] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/14/2021] [Revised: 01/28/2022] [Accepted: 02/19/2022] [Indexed: 02/05/2023]
Abstract
Some patients with schizophrenia have severe cognitive impairment and functional deficits that require long-term institutional care. The patterns of brain-behavior alterations in these individuals, and their differences from patients living successfully in the community, remain poorly understood. Previous cognition-based studies for stratifying schizophrenia patients highlight the importance of subcortical structures in the context of illness heterogeneity. In the present study, subcortical volumes from 96 institutionalized patients with long-term schizophrenia were evaluated using cluster analysis to test for heterogeneity. These data were compared to those from two groups of community-dwelling individuals with schizophrenia for comparison purposes, including 68 long-term ill and 126 first-episode individuals. A total of 290 demographically matched healthy participants were included as normative references at a 1:1 ratio for each patient sample. A subtype of institutionalized patients was identified based on their pattern of subcortical alterations. Using a machine learning algorithm developed to discriminate the two groups of institutionalized patients, all three patient samples were found to have similar rates of patients assigned to the two subtypes (approximately 50% each). In institutionalized patients, only the subtype with the identified pattern of subcortical alterations had greater neocortical and cognitive abnormalities than those in the similarity classified community-dwelling patients with long-term illness. Thus, for the subtype of patients with a distinctive pattern of subcortical alterations, when the distinct pattern of subcortical alterations is present and particularly severe, it is associated with cognitive impairments that may contribute to persistent disability and institutionalization.
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Affiliation(s)
- Qiannan Zhao
- Huaxi MR Research Center, Department of Radiology, West China Hospital of Sichuan University, Chengdu, Sichuan Province, China
- Research Unit of Psychoradiology, Chinese Academy of Medical Sciences, Chengdu, Sichuan Province, China
- Functional and Molecular Imaging Key Laboratory of Sichuan Province, West China Hospital of Sichuan University, Chengdu, Sichuan Province, China
| | - Hengyi Cao
- Huaxi MR Research Center, Department of Radiology, West China Hospital of Sichuan University, Chengdu, Sichuan Province, China
- Center for Psychiatric Neuroscience, Feinstein Institute for Medical Research, Manhasset, NY, USA
- Division of Psychiatry Research, Zucker Hillside Hospital, Glen Oaks, NY, USA
| | - Wenjing Zhang
- Huaxi MR Research Center, Department of Radiology, West China Hospital of Sichuan University, Chengdu, Sichuan Province, China
- Research Unit of Psychoradiology, Chinese Academy of Medical Sciences, Chengdu, Sichuan Province, China
- Functional and Molecular Imaging Key Laboratory of Sichuan Province, West China Hospital of Sichuan University, Chengdu, Sichuan Province, China
| | - Siyi Li
- Huaxi MR Research Center, Department of Radiology, West China Hospital of Sichuan University, Chengdu, Sichuan Province, China
- Research Unit of Psychoradiology, Chinese Academy of Medical Sciences, Chengdu, Sichuan Province, China
- Functional and Molecular Imaging Key Laboratory of Sichuan Province, West China Hospital of Sichuan University, Chengdu, Sichuan Province, China
| | - Yuan Xiao
- Huaxi MR Research Center, Department of Radiology, West China Hospital of Sichuan University, Chengdu, Sichuan Province, China
- Research Unit of Psychoradiology, Chinese Academy of Medical Sciences, Chengdu, Sichuan Province, China
- Functional and Molecular Imaging Key Laboratory of Sichuan Province, West China Hospital of Sichuan University, Chengdu, Sichuan Province, China
| | - Carol A Tamminga
- Department of Psychiatry, University of Texas Southwestern Medical Center, Dallas, TX, USA
| | - Matcheri S Keshavan
- Department of Psychiatry, Beth Israel Deaconess Medical Center and Harvard Medical School, Boston, MA, USA
| | - Godfrey D Pearlson
- Departments of Psychiatry and Neurobiology, Yale University and Olin Neuropsychiatric Research Center, Hartford, CT, USA
| | - Brett A Clementz
- Department of Psychology, University of Georgia, Athens, GA, USA
| | - Elliot S Gershon
- Department of Psychiatry, University of Chicago, Chicago, IL, USA
| | - Scot Kristian Hill
- Department of Psychology, Rosalind Franklin University of Medicine and Science, Chicago, IL, USA
| | - Sarah K Keedy
- Department of Psychiatry, University of Chicago, Chicago, IL, USA
| | - Elena I Ivleva
- Department of Psychiatry, University of Texas Southwestern Medical Center, Dallas, TX, USA
| | - Rebekka Lencer
- Department of Psychiatry and Psychotherapy, University of Münster, Münster, Germany
| | - John A Sweeney
- Huaxi MR Research Center, Department of Radiology, West China Hospital of Sichuan University, Chengdu, Sichuan Province, China
- Department of Psychiatry and Behavioral Neuroscience, University of Cincinnati College of Medicine, Cincinnati, OH, USA
| | - Qiyong Gong
- Huaxi MR Research Center, Department of Radiology, West China Hospital of Sichuan University, Chengdu, Sichuan Province, China.
- Research Unit of Psychoradiology, Chinese Academy of Medical Sciences, Chengdu, Sichuan Province, China.
- Functional and Molecular Imaging Key Laboratory of Sichuan Province, West China Hospital of Sichuan University, Chengdu, Sichuan Province, China.
| | - Su Lui
- Huaxi MR Research Center, Department of Radiology, West China Hospital of Sichuan University, Chengdu, Sichuan Province, China.
- Research Unit of Psychoradiology, Chinese Academy of Medical Sciences, Chengdu, Sichuan Province, China.
- Functional and Molecular Imaging Key Laboratory of Sichuan Province, West China Hospital of Sichuan University, Chengdu, Sichuan Province, China.
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45
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de Brito Robalo BM, de Luca A, Chen C, Dewenter A, Duering M, Hilal S, Koek HL, Kopczak A, Lam BYK, Leemans A, Mok V, Onkenhout LP, van den Brink H, Biessels GJ. Improved sensitivity and precision in multicentre diffusion MRI network analysis using thresholding and harmonization. Neuroimage Clin 2022; 36:103217. [PMID: 36240537 PMCID: PMC9668636 DOI: 10.1016/j.nicl.2022.103217] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/05/2022] [Revised: 08/22/2022] [Accepted: 10/01/2022] [Indexed: 11/06/2022]
Abstract
PURPOSE To investigate if network thresholding and raw data harmonization improve consistency of diffusion MRI (dMRI)-based brain networks while also increasing precision and sensitivity to detect disease effects in multicentre datasets. METHODS Brain networks were reconstructed from dMRI of five samples with cerebral small vessel disease (SVD; 629 patients, 166 controls), as a clinically relevant exemplar condition for studies on network integrity. We evaluated consistency of network architecture in age-matched controls, by calculating cross-site differences in connection probability and fractional anisotropy (FA). Subsequently we evaluated precision and sensitivity to disease effects by identifying connections with low FA in sporadic SVD patients relative to controls, using more severely affected patients with a pure form of genetically defined SVD as reference. RESULTS In controls, thresholding and harmonization improved consistency of network architecture, minimizing cross-site differences in connection probability and FA. In patients relative to controls, thresholding improved precision to detect disrupted connections by removing false positive connections (precision, before: 0.09-0.19; after: 0.38-0.70). Before harmonization, sensitivity was low within individual sites, with few connections surviving multiple testing correction (k = 0-25 connections). Harmonization and pooling improved sensitivity (k = 38), while also achieving higher precision when combined with thresholding (0.97). CONCLUSION We demonstrated that network consistency, precision and sensitivity to detect disease effects in SVD are improved by thresholding and harmonization. We recommend introducing these techniques to leverage large existing multicentre datasets to better understand the impact of disease on brain networks.
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Affiliation(s)
- Bruno M. de Brito Robalo
- Department of Neurology and Neurosurgery, UMC Utrecht Brain Center, University Medical Center Utrecht, Utrecht, the Netherlands,Image Sciences Institute, University Medical Center Utrecht, Utrecht University, Utrecht, the Netherlands
| | - Alberto de Luca
- Department of Neurology and Neurosurgery, UMC Utrecht Brain Center, University Medical Center Utrecht, Utrecht, the Netherlands,Image Sciences Institute, University Medical Center Utrecht, Utrecht University, Utrecht, the Netherlands
| | - Christopher Chen
- Memory, Aging and Cognition Center, Department of Pharmacology, Yong Loo Lin School of Medicine, National University of Singapore, Singapore
| | - Anna Dewenter
- Institute for Stroke and Dementia Research (ISD), University Hospital, LMU Munich, Germany
| | - Marco Duering
- Institute for Stroke and Dementia Research (ISD), University Hospital, LMU Munich, Germany,Medical Image Analysis Center (MIAC AG) and Department of Biomedical Engineering, University of Basel, Basel, Switzerland
| | - Saima Hilal
- Memory, Aging and Cognition Center, Department of Pharmacology, Yong Loo Lin School of Medicine, National University of Singapore, Singapore,Saw Swee Hock School of Public Health, National University of Singapore and National University Health System, Singapore
| | - Huiberdina L. Koek
- Department of Geriatric Medicine, University Medical Center Utrecht, Utrecht, the Netherlands
| | - Anna Kopczak
- Institute for Stroke and Dementia Research (ISD), University Hospital, LMU Munich, Germany
| | - Bonnie Yin Ka Lam
- Division of Neurology, Department of Medicine and Therapeutics, Gerald Choa Neuroscience Centre, Faculty of Medicine, Prince of Wales Hospital, The Chinese University of Hong Kong, Shatin, Hong Kong Special Administrative Region
| | - Alexander Leemans
- Image Sciences Institute, University Medical Center Utrecht, Utrecht University, Utrecht, the Netherlands
| | - Vincent Mok
- Division of Neurology, Department of Medicine and Therapeutics, Gerald Choa Neuroscience Centre, Faculty of Medicine, Prince of Wales Hospital, The Chinese University of Hong Kong, Shatin, Hong Kong Special Administrative Region
| | - Laurien P. Onkenhout
- Department of Neurology and Neurosurgery, UMC Utrecht Brain Center, University Medical Center Utrecht, Utrecht, the Netherlands
| | - Hilde van den Brink
- Department of Neurology and Neurosurgery, UMC Utrecht Brain Center, University Medical Center Utrecht, Utrecht, the Netherlands
| | - Geert Jan Biessels
- Department of Neurology and Neurosurgery, UMC Utrecht Brain Center, University Medical Center Utrecht, Utrecht, the Netherlands,Corresponding author at: Department of Neurology and Neurosurgery, UMC Utrecht Brain Center, University Medical Center Utrecht, P.O. Box 85500, 3508 GA Utrecht, the Netherlands.
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46
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Liu N, Lencer R, Yang Z, Zhang W, Yang C, Zeng J, Sweeney JA, Gong Q, Lui S. Altered functional synchrony between gray and white matter as a novel indicator of brain system dysconnectivity in schizophrenia. Psychol Med 2022; 52:2540-2548. [PMID: 33436114 DOI: 10.1017/s0033291720004420] [Citation(s) in RCA: 3] [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] [Indexed: 02/05/2023]
Abstract
BACKGROUND There is increasing evidence that blood oxygenation level-dependent signaling in white matter (WM) reflects WM functional activity. Whether this activity is altered in schizophrenia remains uncertain, as does whether it is related to established alterations of gray matter (GM) or the microstructure of WM tracts. METHODS A total of 153 antipsychotic-naïve schizophrenia patients and 153 healthy comparison subjects were assessed by resting-state functional magnetic resonance imaging, diffusion tensor imaging, and high-resolution T1-weighted imaging. We tested for case-control differences in the functional activity of WM, and examined their relation to the functional activity of GM and WM microstructure. The relations between fractional anisotropy (FA) in WM and GM-WM functional synchrony were investigated as well. Then, we examined the associations of identified abnormalities to age, duration of untreated psychosis (DUP), and symptom severity. RESULTS Schizophrenia patients displayed reductions of the amplitude of low-frequency fluctuations (ALFF), GM-WM functional synchrony, and FA in widespread regions. Specifically, the genu of corpus callosum not only had weakening in the synchrony of functional activity but also had reduced ALFF and FA. Positive associations were found between FA and functional synchrony in the genu of corpus callosum as well. No significant association was found between identified abnormalities and DUP, and symptom severity. CONCLUSIONS The widespread weakening in the synchrony of functional activity of GM and WM provided novel evidence for functional alterations in schizophrenia. Regarding the WM function as a component of brain systems and investigating its alternation represent a promising direction for future research.
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Affiliation(s)
- Naici Liu
- Huaxi MR Research Center (HMRRC), Department of Radiology, West China Hospital of Sichuan University, Chengdu, China
| | - Rebekka Lencer
- Department of Psychiatry and Psychotherapy, Otto Creutzfeldt Center for Cognitive and Behavioral Neuroscience, University of Muenster, Muenster, Germany
| | - Zhipeng Yang
- College of Electronic Engineering, Chengdu University of Information Technology, Chengdu, PR, China
| | - Wenjing Zhang
- Huaxi MR Research Center (HMRRC), Department of Radiology, West China Hospital of Sichuan University, Chengdu, China
| | - Chengmin Yang
- Huaxi MR Research Center (HMRRC), Department of Radiology, West China Hospital of Sichuan University, Chengdu, China
| | - Jiaxin Zeng
- Huaxi MR Research Center (HMRRC), Department of Radiology, West China Hospital of Sichuan University, Chengdu, China
| | - John A Sweeney
- Huaxi MR Research Center (HMRRC), Department of Radiology, West China Hospital of Sichuan University, Chengdu, China
- Department of Psychiatry and Behavioral Neuroscience, University of Cincinnati, Cincinnati, OH, USA
| | - Qiyong Gong
- Huaxi MR Research Center (HMRRC), Department of Radiology, West China Hospital of Sichuan University, Chengdu, China
| | - Su Lui
- Huaxi MR Research Center (HMRRC), Department of Radiology, West China Hospital of Sichuan University, Chengdu, China
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47
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Seitz-Holland J, Wojcik JD, Cetin-Karayumak S, Lyall AE, Pasternak O, Rathi Y, Vangel M, Pearlson G, Tamminga C, Sweeney JA, Clementz BA, Schretlen DA, Viher PV, Stegmayer K, Walther S, Lee J, Crow T, James A, Voineskos A, Buchanan RW, Szeszko PR, Malhotra AK, Kelly S, Shenton ME, Keshavan MS, Mesholam-Gately RI, Kubicki M. Cognitive deficits, clinical variables, and white matter microstructure in schizophrenia: a multisite harmonization study. Mol Psychiatry 2022; 27:3719-3730. [PMID: 35982257 PMCID: PMC10538303 DOI: 10.1038/s41380-022-01731-3] [Citation(s) in RCA: 11] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/11/2022] [Revised: 07/18/2022] [Accepted: 07/29/2022] [Indexed: 02/08/2023]
Abstract
Cognitive deficits are among the best predictors of real-world functioning in schizophrenia. However, our understanding of how cognitive deficits relate to neuropathology and clinical presentation over the disease lifespan is limited. Here, we combine multi-site, harmonized cognitive, imaging, demographic, and clinical data from over 900 individuals to characterize a) cognitive deficits across the schizophrenia lifespan and b) the association between cognitive deficits, clinical presentation, and white matter (WM) microstructure. Multimodal harmonization was accomplished using T-scores for cognitive data, previously reported standardization methods for demographic and clinical data, and an established harmonization method for imaging data. We applied t-tests and correlation analysis to describe cognitive deficits in individuals with schizophrenia. We then calculated whole-brain WM fractional anisotropy (FA) and utilized regression-mediation analyses to model the association between diagnosis, FA, and cognitive deficits. We observed pronounced cognitive deficits in individuals with schizophrenia (p < 0.006), associated with more positive symptoms and medication dosage. Regression-mediation analyses showed that WM microstructure mediated the association between schizophrenia and language/processing speed/working memory/non-verbal memory. In addition, processing speed mediated the influence of diagnosis and WM microstructure on the other cognitive domains. Our study highlights the critical role of cognitive deficits in schizophrenia. We further show that WM is crucial when trying to understand the role of cognitive deficits, given that it explains the association between schizophrenia and cognitive deficits (directly and via processing speed).
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Affiliation(s)
- Johanna Seitz-Holland
- Department of Psychiatry, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA.
- Department of Psychiatry, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA.
| | - Joanne D Wojcik
- Department of Psychiatry, Beth Israel Deaconess Medical Center and Massachusetts Mental Health Center, Harvard Medical School, Boston, MA, USA
| | - Suheyla Cetin-Karayumak
- Department of Psychiatry, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
| | - Amanda E Lyall
- Department of Psychiatry, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
- Department of Psychiatry, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
| | - Ofer Pasternak
- Department of Psychiatry, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
| | - Yogesh Rathi
- Department of Psychiatry, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
- Department of Psychiatry, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
- Department of Radiology, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
| | - Mark Vangel
- Department of Radiology, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
| | | | - Carol Tamminga
- Department of Psychiatry, UT Southwestern Medical Center, Dallas, TX, USA
| | - John A Sweeney
- Department of Psychiatry and Behavioral Neuroscience, University of Cincinnati, Cincinnati, OH, USA
| | - Brett A Clementz
- Department of Psychology and Neuroscience, Bio-Imaging Research Center, University of Georgia, Athens, GA, USA
| | - David A Schretlen
- Department of Psychiatry and Behavioral Sciences, Morgan Department of Radiology and Radiological Science, Johns Hopkins Medical Institutions, Baltimore, MD, USA
| | - Petra Verena Viher
- University Hospital of Psychiatry and Psychotherapy, University of Bern, Bern, Switzerland
| | - Katharina Stegmayer
- University Hospital of Psychiatry and Psychotherapy, University of Bern, Bern, Switzerland
| | - Sebastian Walther
- University Hospital of Psychiatry and Psychotherapy, University of Bern, Bern, Switzerland
| | - Jungsun Lee
- Department of Psychiatry, University of Ulsan College of Medicine, Asan Medical Center, Seoul, South Korea
| | - Tim Crow
- Department of Psychiatry, SANE POWIC, Warneford Hospital, University of Oxford, Oxford, UK
| | - Anthony James
- Department of Psychiatry, SANE POWIC, Warneford Hospital, University of Oxford, Oxford, UK
| | - Aristotle Voineskos
- Center for Addiction and Mental Health, Department of Psychiatry, University of Toronto, Toronto, ON, Canada
| | - Robert W Buchanan
- Maryland Psychiatric Research Center, Department of Psychiatry, University of Maryland School of Medicine, Baltimore, MD, USA
| | - Philip R Szeszko
- Department of Psychiatry, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- Mental Illness Research, Education and Clinical Center, James J. Peters VA Medical Center, Bronx, New York, NY, USA
| | - Anil K Malhotra
- The Zucker School of Medicine at Hofstra/Northwell, Hempstead, NY, USA
| | - Sinead Kelly
- Department of Psychiatry, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
- Department of Psychiatry, Beth Israel Deaconess Medical Center and Massachusetts Mental Health Center, Harvard Medical School, Boston, MA, USA
| | - Martha E Shenton
- Department of Psychiatry, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
- Department of Psychiatry, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
- Department of Radiology, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
| | - Matcheri S Keshavan
- Department of Psychiatry, Beth Israel Deaconess Medical Center and Massachusetts Mental Health Center, Harvard Medical School, Boston, MA, USA
| | - Raquelle I Mesholam-Gately
- Department of Psychiatry, Beth Israel Deaconess Medical Center and Massachusetts Mental Health Center, Harvard Medical School, Boston, MA, USA
| | - Marek Kubicki
- Department of Psychiatry, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
- Department of Psychiatry, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
- Department of Radiology, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
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48
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Qi S, Sui J, Pearlson G, Bustillo J, Perrone-Bizzozero NI, Kochunov P, Turner JA, Fu Z, Shao W, Jiang R, Yang X, Liu J, Du Y, Chen J, Zhang D, Calhoun VD. Derivation and utility of schizophrenia polygenic risk associated multimodal MRI frontotemporal network. Nat Commun 2022; 13:4929. [PMID: 35995794 PMCID: PMC9395379 DOI: 10.1038/s41467-022-32513-8] [Citation(s) in RCA: 17] [Impact Index Per Article: 5.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/01/2021] [Accepted: 08/03/2022] [Indexed: 12/23/2022] Open
Abstract
Schizophrenia is a highly heritable psychiatric disorder characterized by widespread functional and structural brain abnormalities. However, previous association studies between MRI and polygenic risk were mostly ROI-based single modality analyses, rather than identifying brain-based multimodal predictive biomarkers. Based on schizophrenia polygenic risk scores (PRS) from healthy white people within the UK Biobank dataset (N = 22,459), we discovered a robust PRS-associated brain pattern with smaller gray matter volume and decreased functional activation in frontotemporal cortex, which distinguished schizophrenia from controls with >83% accuracy, and predicted cognition and symptoms across 4 independent schizophrenia cohorts. Further multi-disease comparisons demonstrated that these identified frontotemporal alterations were most severe in schizophrenia and schizo-affective patients, milder in bipolar disorder, and indistinguishable from controls in autism, depression and attention-deficit hyperactivity disorder. These findings indicate the potential of the identified PRS-associated multimodal frontotemporal network to serve as a trans-diagnostic gene intermediated brain biomarker specific to schizophrenia.
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Affiliation(s)
- Shile Qi
- College of Computer Science and Technology, Nanjing University of Aeronautics and Astronautics, Nanjing, China.
| | - Jing Sui
- State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing, China.
| | - Godfrey Pearlson
- Department of Psychiatry, Yale School of Medicine, New Haven, CT, USA
| | - Juan Bustillo
- Departments of Psychiatry and Behavioral Sciences, University of New Mexico, Albuquerque, NM, USA
| | - Nora I Perrone-Bizzozero
- Departments of Psychiatry and Behavioral Sciences, University of New Mexico, Albuquerque, NM, USA
| | - Peter Kochunov
- Department of Psychiatry, University of Maryland School of Medicine, Baltimore, MD, USA
| | - Jessica A Turner
- Department of Psychology, Georgia State University, Atlanta, GA, USA
| | - Zening Fu
- Tri-institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS), [Georgia State University, Georgia Institute of Technology, Emory University], Atlanta, GA, USA
| | - Wei Shao
- College of Computer Science and Technology, Nanjing University of Aeronautics and Astronautics, Nanjing, China
| | - Rongtao Jiang
- Department of Radiology and Biomedical Imaging, Yale University, New Haven, CT, USA
| | - Xiao Yang
- Huaxi Brain Research Center, West China Hospital of Sichuan University, Chengdu, China
| | - Jingyu Liu
- Tri-institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS), [Georgia State University, Georgia Institute of Technology, Emory University], Atlanta, GA, USA
| | - Yuhui Du
- School of Computer & Information Technology, Shanxi University, Taiyuan, China
| | - Jiayu Chen
- Tri-institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS), [Georgia State University, Georgia Institute of Technology, Emory University], Atlanta, GA, USA.
| | - Daoqiang Zhang
- College of Computer Science and Technology, Nanjing University of Aeronautics and Astronautics, Nanjing, China.
| | - Vince D Calhoun
- Tri-institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS), [Georgia State University, Georgia Institute of Technology, Emory University], Atlanta, GA, USA
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49
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Zeng J, Zhang W, Wu G, Wang X, Shah C, Li S, Xiao Y, Yao L, Cao H, Li Z, Sweeney JA, Lui S, Gong Q. Effects of Antipsychotic Medications and Illness Duration on Brain Features That Distinguish Schizophrenia Patients. Schizophr Bull 2022; 48:1354-1362. [PMID: 35925035 PMCID: PMC9673268 DOI: 10.1093/schbul/sbac094] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 02/05/2023]
Abstract
BACKGROUND AND HYPOTHESIS Previous studies have reported effects of antipsychotic treatment and illness duration on brain features. This study used a machine learning approach to examine whether these factors in aggregate impacted the utility of MRI features for differentiating individual schizophrenia patients from healthy controls. STUDY DESIGN This case-control study used patients with never-treated first-episode schizophrenia (FES, n = 179) and long-term ill schizophrenia (LTSZ, n = 30), with follow-up of the FES group after treatment (n = 71), a group of patients who had received long-term antipsychotic treatment (n = 93) and age and sex-matched healthy controls (n = 373) for each patient group. A multiple kernel learning classifier combining both structural and functional brain features was used to discriminate individual patients from controls. STUDY RESULTS MRI features differentiated untreated FES (0.73) and LTSZ (0.83) patients from healthy controls with moderate accuracy, but accuracy was significantly higher in antipsychotic-treated FES (0.94) and LTSZ (0.98) patients. Treatment was associated with significantly increased accuracy of case identification in both early course and long-term ill patients (both p < .001). Effects of illness duration, examined separately in treated and untreated patients, were less robust. CONCLUSIONS Our results demonstrate that initiation of antipsychotic treatment alters brain features in ways that further distinguish individual schizophrenia patients from healthy individuals, and have a modest effect of illness duration. Intrinsic illness-related brain alterations in untreated patients, regardless of illness duration, are not sufficiently robust for accurate identification of schizophrenia patients.
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Affiliation(s)
| | | | - Guorong Wu
- Key Laboratory of Cognition and Personality, Faculty of Psychology, Southwest University, Chongqing, China
| | - Xiaowan Wang
- Key Laboratory of Cognition and Personality, Faculty of Psychology, Southwest University, Chongqing, China
| | - Chandan Shah
- Department of Radiology, Huaxi MR Research Center (HMRRC), Functional and Molecular Imaging Key Laboratory of Sichuan Province, West China Hospital of Sichuan University, Chengdu, China
| | - Siyi Li
- Department of Radiology, West China Hospital of Sichuan University, Chengdu, China
| | - Yuan Xiao
- Department of Radiology, West China Hospital of Sichuan University, Chengdu, China
| | - Li Yao
- Department of Radiology, West China Hospital of Sichuan University, Chengdu, China
| | - Hengyi Cao
- Department of Radiology, Huaxi MR Research Center (HMRRC), Functional and Molecular Imaging Key Laboratory of Sichuan Province, West China Hospital of Sichuan University, Chengdu, China,Center for Psychiatry Neuroscience, Feinstein Institute for Medical Research, Manhasset, NY, USA,Division of Psychiatry Research, Zucker Hillside Hospital, Glen Oaks, NY, USA
| | - Zhenlin Li
- Department of Radiology, West China Hospital of Sichuan University, Chengdu, China
| | - John A Sweeney
- Department of Radiology, Huaxi MR Research Center (HMRRC), Functional and Molecular Imaging Key Laboratory of Sichuan Province, West China Hospital of Sichuan University, Chengdu, China,Department of Psychiatry and Behavioral Neuroscience, University of Cincinnati College of Medicine, Cincinnati, OH, USA
| | - Su Lui
- To whom correspondence should be addressed; Department of Radiology, West China Hospital of Sichuan University, Chengdu 610041, China, tel/fax: +86-28-85423960; e-mail:
| | - Qiyong Gong
- Department of Radiology, West China Hospital of Sichuan University, Chengdu, China,Department of Radiology, Huaxi MR Research Center (HMRRC), Functional and Molecular Imaging Key Laboratory of Sichuan Province, West China Hospital of Sichuan University, Chengdu, China
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50
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Avery SN, Huang AS, Sheffield JM, Rogers BP, Vandekar S, Anticevic A, Woodward ND. Development of Thalamocortical Structural Connectivity in Typically Developing and Psychosis Spectrum Youths. BIOLOGICAL PSYCHIATRY. COGNITIVE NEUROSCIENCE AND NEUROIMAGING 2022; 7:782-792. [PMID: 34655804 PMCID: PMC9008075 DOI: 10.1016/j.bpsc.2021.09.009] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/14/2021] [Revised: 09/30/2021] [Accepted: 09/30/2021] [Indexed: 12/13/2022]
Abstract
BACKGROUND Thalamocortical white matter connectivity is disrupted in psychosis and is hypothesized to play a role in its etiology and associated cognitive impairment. Attenuated cognitive symptoms often begin in adolescence, during a critical phase of white matter and cognitive development. However, little is known about the development of thalamocortical white matter connectivity and its association with cognition. METHODS This study characterized effects of age, sex, psychosis symptomatology, and cognition in thalamocortical networks in a large sample of youths (N = 1144, ages 8-22 years, 46% male) from the Philadelphia Neurodevelopmental Cohort, which included 316 typically developing youths, 330 youths on the psychosis spectrum, and 498 youths with other psychopathology. Probabilistic tractography was used to quantify percent total connectivity between the thalamus and six cortical regions and assess microstructural properties (i.e., fractional anisotropy) of thalamocortical white matter tracts. RESULTS Overall, percent total connectivity of the thalamus was weakly associated with age and was not associated with psychopathology or cognition. In contrast, fractional anisotropy of all thalamocortical tracts increased significantly with age, was generally higher in males than females, and was lowest in youths on the psychosis spectrum. Fractional anisotropy of tracts linking the thalamus to prefrontal and posterior parietal cortices was related to better cognitive function across subjects. CONCLUSIONS By characterizing the pattern of typical development and alterations in those at risk for psychotic disorders, this study provides a foundation for further conceptualization of thalamocortical white matter microstructure as a marker of neurodevelopment supporting cognition and an important risk marker for psychosis.
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Affiliation(s)
- Suzanne N Avery
- Department of Psychiatry and Behavioral Sciences, Vanderbilt University Medical Center, Nashville, Tennessee.
| | - Anna S Huang
- Department of Psychiatry and Behavioral Sciences, Vanderbilt University Medical Center, Nashville, Tennessee
| | - Julia M Sheffield
- Department of Psychiatry and Behavioral Sciences, Vanderbilt University Medical Center, Nashville, Tennessee
| | - Baxter P Rogers
- Vanderbilt University Institute of Imaging Sciences, Nashville, Tennessee
| | - Simon Vandekar
- Department of Biostatistics, Vanderbilt University Medical Center, Nashville, Tennessee
| | - Alan Anticevic
- Department of Psychiatry, Yale University School of Medicine, New Haven, Connecticut
| | - Neil D Woodward
- Department of Psychiatry and Behavioral Sciences, Vanderbilt University Medical Center, Nashville, Tennessee
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