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Dworetsky A, Seitzman BA, Adeyemo B, Nielsen AN, Hatoum AS, Smith DM, Nichols TE, Neta M, Petersen SE, Gratton C. Two common and distinct forms of variation in human functional brain networks. Nat Neurosci 2024; 27:1187-1198. [PMID: 38689142 PMCID: PMC11248096 DOI: 10.1038/s41593-024-01618-2] [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: 02/28/2023] [Accepted: 03/07/2024] [Indexed: 05/02/2024]
Abstract
The cortex has a characteristic layout with specialized functional areas forming distributed large-scale networks. However, substantial work shows striking variation in this organization across people, which relates to differences in behavior. While most previous work treats individual differences as linked to boundary shifts between the borders of regions, here we show that cortical 'variants' also occur at a distance from their typical position, forming ectopic intrusions. Both 'border' and 'ectopic' variants are common across individuals, but differ in their location, network associations, properties of subgroups of individuals, activations during tasks, and prediction of behavioral phenotypes. Border variants also track significantly more with shared genetics than ectopic variants, suggesting a closer link between ectopic variants and environmental influences. This work argues that these two dissociable forms of variation-border shifts and ectopic intrusions-must be separately accounted for in the analysis of individual differences in cortical systems across people.
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Affiliation(s)
- Ally Dworetsky
- Department of Radiology, Washington University School of Medicine, St. Louis, MO, USA
- Department of Psychology, Florida State University, Tallahassee, FL, USA
- Department of Psychology, Northwestern University, Evanston, IL, USA
| | - Benjamin A Seitzman
- Department of Radiation Oncology, Washington University School of Medicine, St. Louis, MO, USA
| | - Babatunde Adeyemo
- Department of Neurology, Washington University School of Medicine, St. Louis, MO, USA
| | - Ashley N Nielsen
- Department of Neurology, Washington University School of Medicine, St. Louis, MO, USA
| | - Alexander S Hatoum
- Department of Psychological and Brain Sciences, Washington University School of Medicine, St. Louis, MO, USA
| | - Derek M Smith
- Department of Psychology, Northwestern University, Evanston, IL, USA
- Department of Neurology, Division of Cognitive Neurology/Neuropsychology, The Johns Hopkins University School of Medicine, Baltimore, MD, USA
| | - Thomas E Nichols
- Big Data Institute, Li Ka Shing Centre for Health Information and Discovery, Nuffield Department of Population Health, University of Oxford, Oxford, United Kingdom
- Wellcome Centre for Integrative Neuroimaging, FMRIB, Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, United Kingdom
| | - Maital Neta
- Department of Psychology, University of Nebraska-Lincoln, Lincoln, NE, USA
- Center for Brain, Biology, and Behavior, University of Nebraska-Lincoln, Lincoln, NE, USA
| | - Steven E Petersen
- Department of Radiology, Washington University School of Medicine, St. Louis, MO, USA
- Department of Neurology, Washington University School of Medicine, St. Louis, MO, USA
- Department of Psychological and Brain Sciences, Washington University School of Medicine, St. Louis, MO, USA
- Department of Neuroscience, Washington University School of Medicine, St. Louis, MO, USA
- Department of Biomedical Engineering, Washington University School of Medicine, St. Louis, MO, USA
| | - Caterina Gratton
- Department of Psychology, Florida State University, Tallahassee, FL, USA.
- Department of Psychology, Northwestern University, Evanston, IL, USA.
- Neuroscience Program, Florida State University, Tallahassee, FL, USA.
- Department of Neurology, Northwestern University, Evanston, IL, USA.
- Interdepartmental Neuroscience Program, Northwestern University, Evanston, IL, USA.
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2
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Dahmani L, Bai Y, Zhang W, Ren J, Li S, Hu Q, Fu X, Ma J, Wei W, Wang M, Liu H, Wang D. Individualized functional connectivity markers associated with motor and mood symptoms of Parkinson's disease. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.01.31.578238. [PMID: 38352322 PMCID: PMC10862849 DOI: 10.1101/2024.01.31.578238] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 02/23/2024]
Abstract
Parkinson's disease (PD) is a complex neurological disorder characterized by many motor and non-motor symptoms. While most studies focus on the motor symptoms of the disease, it is important to identify markers that underlie different facets of the disease. In this case-control study, we sought to discover reliable, individualized functional connectivity markers associated with both motor and mood symptoms of PD. Using functional MRI, we extensively sampled 166 patients with PD (64 women, 102 men; mean age=61.8 years, SD=7.81) and 51 healthy control participants (32 women, 19 men; mean age=55.68 years, SD=7.62). We found that a model consisting of 44 functional connections predicted both motor (UPDRS-III: Pearson r=0.21, FDR-adjusted p=0.006) and mood symptoms (HAMD: Pearson r=0.23, FDR-adjusted p=0.006; HAMA: Pearson r=0.21, FDR-adjusted p=0.006). Two sets of connections contributed differentially to these predictions. Between-network connections, mainly connecting the sensorimotor and visual large-scale functional networks, substantially contributed to the prediction of motor measures, while within-network connections in the insula and sensorimotor network contributed more so to mood prediction. The middle to posterior insula region played a particularly important role in predicting depression and anxiety scores. We successfully replicated and generalized our findings in two independent PD datasets. Taken together, our findings indicate that sensorimotor and visual network markers are indicative of PD brain pathology, and that distinct subsets of markers are associated with motor and mood symptoms of PD.
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Affiliation(s)
- Louisa Dahmani
- Department of Medical Imaging, Henan Provincial People’s Hospital, Zhengzhou, Henan, China
- Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Harvard Medical School, Charlestown, MA, USA, 02129
| | - Yan Bai
- Department of Medical Imaging, Henan Provincial People’s Hospital, Zhengzhou, Henan, China
| | - Wei Zhang
- Changping Laboratory, Beijing, China
| | | | - Shiyi Li
- Changping Laboratory, Beijing, China
| | - Qingyu Hu
- Changping Laboratory, Beijing, China
| | | | - Jianjun Ma
- Department of Neurology, Henan Provincial People’s Hospital, Zhengzhou, Henan, China
| | - Wei Wei
- Department of Medical Imaging, Henan Provincial People’s Hospital, Zhengzhou, Henan, China
| | - Meiyun Wang
- Department of Medical Imaging, Henan Provincial People’s Hospital, Zhengzhou, Henan, China
| | - Hesheng Liu
- Changping Laboratory, Beijing, China
- Biomedical Pioneering Innovation Center, Peking University, Beijing, China
| | - Danhong Wang
- Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Harvard Medical School, Charlestown, MA, USA, 02129
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3
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Luo L, You W, DelBello MP, Gong Q, Li F. Recent advances in psychoradiology. Phys Med Biol 2022; 67. [PMID: 36279868 DOI: 10.1088/1361-6560/ac9d1e] [Citation(s) in RCA: 20] [Impact Index Per Article: 10.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/04/2022] [Accepted: 10/24/2022] [Indexed: 11/24/2022]
Abstract
Abstract
Psychiatry, as a field, lacks objective markers for diagnosis, progression, treatment planning, and prognosis, in part due to difficulties studying the brain in vivo, and diagnoses are based on self-reported symptoms and observation of patient behavior and cognition. Rapid advances in brain imaging techniques allow clinical investigators to noninvasively quantify brain features at the structural, functional, and molecular levels. Psychoradiology is an emerging discipline at the intersection of psychiatry and radiology. Psychoradiology applies medical imaging technologies to psychiatry and promises not only to improve insight into structural and functional brain abnormalities in patients with psychiatric disorders but also to have potential clinical utility. We searched for representative studies related to recent advances in psychoradiology through May 1, 2022, and conducted a selective review of 165 references, including 75 research articles. We summarize the novel dynamic imaging processing methods to model brain networks and present imaging genetics studies that reveal the relationship between various neuroimaging endophenotypes and genetic markers in psychiatric disorders. Furthermore, we survey recent advances in psychoradiology, with a focus on future psychiatric diagnostic approaches with dimensional analysis and a shift from group-level to individualized analysis. Finally, we examine the application of machine learning in psychoradiology studies and the potential of a novel option for brain stimulation treatment based on psychoradiological findings in precision medicine. Here, we provide a summary of recent advances in psychoradiology research, and we hope this review will help guide the practice of psychoradiology in the scientific and clinical fields.
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4
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Zhang J, Zhao T, Zhang J, Zhang Z, Li H, Cheng B, Pang Y, Wu H, Wang J. Prediction of childhood maltreatment and subtypes with personalized functional connectome of large-scale brain networks. Hum Brain Mapp 2022; 43:4710-4721. [PMID: 35735128 PMCID: PMC9491288 DOI: 10.1002/hbm.25985] [Citation(s) in RCA: 13] [Impact Index Per Article: 6.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/22/2022] [Revised: 05/12/2022] [Accepted: 05/24/2022] [Indexed: 12/20/2022] Open
Abstract
Childhood maltreatment (CM) has a long impact on physical and mental health of children. However, the neural underpinnings of CM are still unclear. In this study, we aimed to establish the associations between functional connectome of large-scale brain networks and influences of CM evaluated through Childhood Trauma Questionnaire (CTQ) at the individual level based on resting-state functional magnetic resonance imaging data of 215 adults. A novel individual functional mapping approach was employed to identify subject-specific functional networks and functional network connectivities (FNCs). A connectome-based predictive modeling (CPM) was used to estimate CM total and subscale scores using individual FNCs. The CPM established with FNCs can well predict CM total scores and subscale scores including emotion abuse, emotion neglect, physical abuse, physical neglect, and sexual abuse. These FNCs primarily involve default mode network, fronto-parietal network, visual network, limbic network, motor network, dorsal and ventral attention networks, and different networks have distinct contributions to predicting CM and subtypes. Moreover, we found that CM showed age and sex effects on individual functional connections. Taken together, the present findings revealed that different types of CM are associated with different atypical neural networks which provide new clues to understand the neurobiological consequences of childhood adversity.
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Affiliation(s)
- Jiang Zhang
- College of Electrical EngineeringSichuan UniversityChengduChina
- Med‐X Center for InformaticsSichuan UniversityChengduChina
| | - Tianyu Zhao
- College of Electrical EngineeringSichuan UniversityChengduChina
| | - Jingyue Zhang
- College of Electrical EngineeringSichuan UniversityChengduChina
| | - Zhiwei Zhang
- College of Electrical EngineeringSichuan UniversityChengduChina
| | - Hongming Li
- Department of RadiologyUniversity of PennsylvaniaPhiladelphiaPennsylvaniaUSA
| | - Bochao Cheng
- Department of RadiologyWest China Second University Hospital of Sichuan UniversityChengduChina
| | - Yajing Pang
- School of Electrical EngineeringZhengzhou UniversityZhengzhouChina
| | - Huawang Wu
- The Affiliated Brain Hospital of Guangzhou Medical University (Guangzhou Huiai Hospital)GuangzhouChina
| | - Jiaojian Wang
- State Key Laboratory of Primate Biomedical Research, Institute of Primate Translational MedicineKunming University of Science and TechnologyKunmingChina
- Yunnan Key Laboratory of Primate Biomedical ResearchKunmingYunnanChina
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Revell AY, Silva AB, Arnold TC, Stein JM, Das SR, Shinohara RT, Bassett DS, Litt B, Davis KA. A framework For brain atlases: Lessons from seizure dynamics. Neuroimage 2022; 254:118986. [PMID: 35339683 PMCID: PMC9342687 DOI: 10.1016/j.neuroimage.2022.118986] [Citation(s) in RCA: 13] [Impact Index Per Article: 6.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/26/2021] [Revised: 01/13/2022] [Accepted: 02/07/2022] [Indexed: 01/03/2023] Open
Abstract
Brain maps, or atlases, are essential tools for studying brain function and organization. The abundance of available atlases used across the neuroscience literature, however, creates an implicit challenge that may alter the hypotheses and predictions we make about neurological function and pathophysiology. Here, we demonstrate how parcellation scale, shape, anatomical coverage, and other atlas features may impact our prediction of the brain's function from its underlying structure. We show how network topology, structure-function correlation (SFC), and the power to test specific hypotheses about epilepsy pathophysiology may change as a result of atlas choice and atlas features. Through the lens of our disease system, we propose a general framework and algorithm for atlas selection. This framework aims to maximize the descriptive, explanatory, and predictive validity of an atlas. Broadly, our framework strives to provide empirical guidance to neuroscience research utilizing the various atlases published over the last century.
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Affiliation(s)
- Andrew Y Revell
- Department of Neuroscience, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA; Center for Neuroengineering and Therapeutics, University of Pennsylvania, Philadelphia, PA 19104, USA.
| | - Alexander B Silva
- Center for Neuroengineering and Therapeutics, University of Pennsylvania, Philadelphia, PA 19104, USA; Department of Bioengineering, School of Engineering and Applied Science, University of Pennsylvania, Philadelphia, PA 19104, USA; Medical Scientist Training Program, University of California, San Francisco, CA 94143, USA
| | - T Campbell Arnold
- Center for Neuroengineering and Therapeutics, University of Pennsylvania, Philadelphia, PA 19104, USA; Department of Bioengineering, School of Engineering and Applied Science, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Joel M Stein
- Center for Neuroengineering and Therapeutics, University of Pennsylvania, Philadelphia, PA 19104, USA; Department of Radiology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Sandhitsu R Das
- Center for Neuroengineering and Therapeutics, University of Pennsylvania, Philadelphia, PA 19104, USA; Department of Neurology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Russell T Shinohara
- Department of Biostatistics, Epidemiology, and Informatics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA; Penn Statistics in Imaging and Visualization Endeavor, Perelman school of Medicine, University of Pennsylvania, PA 19104, USA
| | - Dani S Bassett
- Department of Neuroscience, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA; Center for Neuroengineering and Therapeutics, University of Pennsylvania, Philadelphia, PA 19104, USA; Department of Bioengineering, School of Engineering and Applied Science, University of Pennsylvania, Philadelphia, PA 19104, USA; Department of Neurology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA; Center for Biomedical Image Computing and Analytics, Perelman School of Medicine, University of Pennsylvania, PA 19104, USA; Department of Electrical and Systems Engineering, School of Engineering and Applied Science, University of Pennsylvania, Philadelphia, PA 19104, USA; Department of Physics and Astronomy, College of Arts and Sciences, University of Pennsylvania, Philadelphia, PA 19104, USA; Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA; Santa Fe Institute, Santa Fe, NM 87501, USA
| | - Brian Litt
- Center for Neuroengineering and Therapeutics, University of Pennsylvania, Philadelphia, PA 19104, USA; Department of Bioengineering, School of Engineering and Applied Science, University of Pennsylvania, Philadelphia, PA 19104, USA; Department of Neurology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Kathryn A Davis
- Department of Neuroscience, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA; Center for Neuroengineering and Therapeutics, University of Pennsylvania, Philadelphia, PA 19104, USA; Department of Neurology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA
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6
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Li J, Wu GR, Li B, Fan F, Zhao X, Meng Y, Zhong P, Yang S, Biswal BB, Chen H, Liao W. Transcriptomic and macroscopic architectures of intersubject functional variability in human brain white-matter. Commun Biol 2021; 4:1417. [PMID: 34931033 PMCID: PMC8688465 DOI: 10.1038/s42003-021-02952-y] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/01/2021] [Accepted: 11/30/2021] [Indexed: 12/18/2022] Open
Abstract
Intersubject variability is a fundamental characteristic of brain organizations, and not just "noise". Although intrinsic functional connectivity (FC) is unique to each individual and varies across brain gray-matter, the underlying mechanisms of intersubject functional variability in white-matter (WM) remain unknown. This study identified WMFC variabilities and determined the genetic basis and macroscale imaging in 45 healthy subjects. The functional localization pattern of intersubject variability across WM is heterogeneous, with most variability observed in the heteromodal cortex. The variabilities of heteromodal regions in expression profiles of genes are related to neuronal cells, involved in synapse-related and glutamic pathways, and associated with psychiatric disorders. In contrast, genes overexpressed in unimodal regions are mostly expressed in glial cells and were related to neurological diseases. Macroscopic variability recapitulates the functional and structural specializations and behavioral phenotypes. Together, our results provide clues to intersubject variabilities of the WMFC with convergent transcriptomic and cellular signatures, which relate to macroscale brain specialization.
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Affiliation(s)
- Jiao Li
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Laboratory for Neuroinformation, University of Electronic Science and Technology of China, Chengdu, 611731, P.R. China
- The Center of Psychosomatic Medicine, Sichuan Provincial Center for Mental Health, Sichuan Provincial People's Hospital, University of Electronic Science and Technology of China, Chengdu, 611731, P.R. China
- School of Life Science and Technology, Center for Information in BioMedicine, University of Electronic Science and Technology of China, Chengdu, 611731, P.R. China
| | - Guo-Rong Wu
- Key Laboratory of Cognition and Personality, Faculty of Psychology, Southwest University, Chongqing, 400715, P.R. China
| | - Bing Li
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Laboratory for Neuroinformation, University of Electronic Science and Technology of China, Chengdu, 611731, P.R. China
- School of Life Science and Technology, Center for Information in BioMedicine, University of Electronic Science and Technology of China, Chengdu, 611731, P.R. China
| | - Feiyang Fan
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Laboratory for Neuroinformation, University of Electronic Science and Technology of China, Chengdu, 611731, P.R. China
- School of Life Science and Technology, Center for Information in BioMedicine, University of Electronic Science and Technology of China, Chengdu, 611731, P.R. China
| | - Xiaopeng Zhao
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Laboratory for Neuroinformation, University of Electronic Science and Technology of China, Chengdu, 611731, P.R. China
- School of Life Science and Technology, Center for Information in BioMedicine, University of Electronic Science and Technology of China, Chengdu, 611731, P.R. China
| | - Yao Meng
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Laboratory for Neuroinformation, University of Electronic Science and Technology of China, Chengdu, 611731, P.R. China
- School of Life Science and Technology, Center for Information in BioMedicine, University of Electronic Science and Technology of China, Chengdu, 611731, P.R. China
| | - Peng Zhong
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Laboratory for Neuroinformation, University of Electronic Science and Technology of China, Chengdu, 611731, P.R. China
- School of Life Science and Technology, Center for Information in BioMedicine, University of Electronic Science and Technology of China, Chengdu, 611731, P.R. China
| | - Siqi Yang
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Laboratory for Neuroinformation, University of Electronic Science and Technology of China, Chengdu, 611731, P.R. China
- School of Life Science and Technology, Center for Information in BioMedicine, University of Electronic Science and Technology of China, Chengdu, 611731, P.R. China
| | - Bharat B Biswal
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Laboratory for Neuroinformation, University of Electronic Science and Technology of China, Chengdu, 611731, P.R. China
- School of Life Science and Technology, Center for Information in BioMedicine, University of Electronic Science and Technology of China, Chengdu, 611731, P.R. China
- Department of Biomedical Engineering, New Jersey Institute of Technology, Newark, NJ, 07103, USA
| | - Huafu Chen
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Laboratory for Neuroinformation, University of Electronic Science and Technology of China, Chengdu, 611731, P.R. China.
- The Center of Psychosomatic Medicine, Sichuan Provincial Center for Mental Health, Sichuan Provincial People's Hospital, University of Electronic Science and Technology of China, Chengdu, 611731, P.R. China.
- School of Life Science and Technology, Center for Information in BioMedicine, University of Electronic Science and Technology of China, Chengdu, 611731, P.R. China.
| | - Wei Liao
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Laboratory for Neuroinformation, University of Electronic Science and Technology of China, Chengdu, 611731, P.R. China.
- School of Life Science and Technology, Center for Information in BioMedicine, University of Electronic Science and Technology of China, Chengdu, 611731, P.R. China.
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McLaughlin NC, Dougherty DD, Eskandar E, Ward H, Foote KD, Malone DA, Machado A, Wong W, Sedrak M, Goodman W, Kopell BH, Issa F, Shields DC, Abulseoud OA, Lee K, Frye MA, Widge AS, Deckersbach T, Okun MS, Bowers D, Bauer RM, Mason D, Kubu CS, Bernstein I, Lapidus K, Rosenthal DL, Jenkins RL, Read C, Malloy PF, Salloway S, Strong DR, Jones RN, Rasmussen SA, Greenberg BD. Double blind randomized controlled trial of deep brain stimulation for obsessive-compulsive disorder: Clinical trial design. Contemp Clin Trials Commun 2021; 22:100785. [PMID: 34189335 PMCID: PMC8219641 DOI: 10.1016/j.conctc.2021.100785] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/09/2020] [Revised: 04/14/2021] [Accepted: 05/16/2021] [Indexed: 12/13/2022] Open
Abstract
Obsessive-compulsive disorder (OCD), a leading cause of disability, affects ~1–2% of the population, and can be distressing and disabling. About 1/3 of individuals demonstrate poor responsiveness to conventional treatments. A small proportion of these individuals may be deep brain stimulation (DBS) candidates. Candidacy is assessed through a multidisciplinary process including assessment of illness severity, chronicity, and functional impact. Optimization failure, despite multiple treatments, is critical during screening. Few patients nationwide are eligible for OCD DBS and thus a multi-center approach was necessary to obtain adequate sample size. The study was conducted over a six-year period and was a NIH-funded, eight-center sham-controlled trial of DBS targeting the ventral capsule/ventral striatum (VC/VS) region. There were 269 individuals who initially contacted the sites, in order to achieve 27 participants enrolled. Study enrollment required extensive review for eligibility, which was overseen by an independent advisory board. Disabling OCD had to be persistent for ≥5 years despite exhaustive medication and behavioral treatment. The final cohort was derived from a detailed consent process that included consent monitoring. Mean illness duration was 27.2 years. OCD symptom subtypes and psychiatric comorbidities varied, but all had severe disability with impaired quality of life and functioning. Participants were randomized to receive sham or active DBS for three months. Following this period, all participants received active DBS. Treatment assignment was masked to participants and raters and assessments were blinded. The final sample was consistent in demographic characteristics and clinical features when compared to other contemporary published prospective studies of OCD DBS. We report the clinical trial design, methods, and general demographics of this OCD DBS sample.
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Affiliation(s)
- Nicole C.R. McLaughlin
- Butler Hospital, 345 Blackstone Blvd, Providence, RI, 02906, USA
- Alpert Medical School of Brown University, Department of Psychiatry and Human Behavior, Providence, RI, USA
- Corresponding author. Alpert Medical School of Brown University Butler Hospital, 345 Blackstone Blvd. Providence, RI, 02906, USA.
| | - Darin D. Dougherty
- Massachusetts General Hospital, 149 13th Street; Charlestown, MA, 02129, USA
- Harvard Medical School, 25 Shattuck St., Boston, MA, 02115, USA
| | - Emad Eskandar
- Massachusetts General Hospital, 149 13th Street; Charlestown, MA, 02129, USA
- Harvard Medical School, 25 Shattuck St., Boston, MA, 02115, USA
| | - Herbert Ward
- Department of Psychiatry, UF Health Springhill, University of Florida, 4037 NW 86th Terrace, Gainesville, FL, 32606, USA
| | - Kelly D. Foote
- Norman Fixel Institute of Neurological Diseases, Department of Neurology, University of Florida, 3009 SW Williston Dr., Gainesville, FL, 32608, USA
| | - Donald A. Malone
- Cleveland Clinic Neurological Institute, 9500 Euclid Ave., Cleveland, OH, 44195, USA
| | - Andre Machado
- Cleveland Clinic Neurological Institute, 9500 Euclid Ave., Cleveland, OH, 44195, USA
| | - William Wong
- Kaiser Permanente, 1100 Veterans Blvd., Redwood City, CA, 94063, USA
| | - Mark Sedrak
- Kaiser Permanente, Department of Neurosurgery, 1150 Veterans Blvd., Redwood City, CA, 94063, USA
| | - Wayne Goodman
- Department of Psychiatry, Icahn School of Medicine at Mount Sinai, 1000 10th Avenue, New York, NY, 10011, USA
| | - Brian H. Kopell
- Department of Psychiatry, Icahn School of Medicine at Mount Sinai, 1000 10th Avenue, New York, NY, 10011, USA
| | - Fuad Issa
- Department of Psychiatry & Behavioral Sciences, School of Medicine & Health Sciences, George Washington University, 2120 L Street, NW, Suite 600, Washington, DC, 20037, USA
| | - Donald C. Shields
- Department of Neurosurgery, The George Washington University, 2150 Pennsylvania Ave., NW, Ste. 7-409 Washington, DC, 20037, USA
| | - Osama A. Abulseoud
- Neuroimaging Research Branch at the National Institute on Drug Abuse, 251 Bayview Boulevard, Baltimore, MD, 21224, USA
| | - Kendall Lee
- Mayo Clinic College of Medicine, 200 First Street SW, Rochester MN, 55901, USA
| | - Mark A. Frye
- Mayo Clinic College of Medicine, 200 First Street SW, Rochester MN, 55901, USA
| | - Alik S. Widge
- Massachusetts General Hospital, 149 13th Street; Charlestown, MA, 02129, USA
- Harvard Medical School, 25 Shattuck St., Boston, MA, 02115, USA
- Massachusetts Institute of Technology, 77 Massachusetts Ave, Cambridge, MA, 02139, USA
| | - Thilo Deckersbach
- University of Applied Sciences Europe, Dessauer Str. 3-5, 10963, Berlin, Germany
| | - Michael S. Okun
- Norman Fixel Institute of Neurological Diseases, Department of Neurology, University of Florida, 3009 SW Williston Dr., Gainesville, FL, 32608, USA
| | - Dawn Bowers
- Department of Clinical & Health Psychology, University of Florida, PO Box 100165, Gainesville, FL, 32610, USA
| | - Russell M. Bauer
- Department of Clinical & Health Psychology, University of Florida, PO Box 100165, Gainesville, FL, 32610, USA
| | - Dana Mason
- Department of Psychiatry, UF Health Springhill, University of Florida, 4037 NW 86th Terrace, Gainesville, FL, 32606, USA
| | - Cynthia S. Kubu
- Cleveland Clinic Neurological Institute, 9500 Euclid Ave., Cleveland, OH, 44195, USA
| | - Ivan Bernstein
- Kaiser Permanente, 1100 Veterans Blvd., Redwood City, CA, 94063, USA
| | - Kyle Lapidus
- Northwell Health, 300 West 72 Street, #1D, New York, NY, 10023, USA
| | - David L. Rosenthal
- Department of Psychiatry, Icahn School of Medicine at Mount Sinai, 1000 10th Avenue, New York, NY, 10011, USA
| | - Robert L. Jenkins
- Department of Psychiatry & Behavioral Sciences, School of Medicine & Health Sciences, George Washington University, 2120 L Street, NW, Suite 600, Washington, DC, 20037, USA
| | - Cynthia Read
- Butler Hospital, 345 Blackstone Blvd, Providence, RI, 02906, USA
| | - Paul F. Malloy
- Butler Hospital, 345 Blackstone Blvd, Providence, RI, 02906, USA
- Alpert Medical School of Brown University, Department of Psychiatry and Human Behavior, Providence, RI, USA
| | - Stephen Salloway
- Butler Hospital, 345 Blackstone Blvd, Providence, RI, 02906, USA
- Alpert Medical School of Brown University, Department of Psychiatry and Human Behavior, Providence, RI, USA
| | - David R. Strong
- Department of Family Medicine and Public Health, University of California, San Diego, 9500 Gilman Drive, La Jolla, Ca, 92093, USA
| | - Richard N. Jones
- Alpert Medical School of Brown University, Department of Psychiatry and Human Behavior, Providence, RI, USA
| | - Steven A. Rasmussen
- Butler Hospital, 345 Blackstone Blvd, Providence, RI, 02906, USA
- Alpert Medical School of Brown University, Department of Psychiatry and Human Behavior, Providence, RI, USA
| | - Benjamin D. Greenberg
- Butler Hospital, 345 Blackstone Blvd, Providence, RI, 02906, USA
- Alpert Medical School of Brown University, Department of Psychiatry and Human Behavior, Providence, RI, USA
- Center for Neurorestoration & Neurotechnology, Providence VA Medical Center, 830 Chalkstone Ave., Bldg 32, Providence, RI, 02908, USA
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Lebois LAM, Li M, Baker JT, Wolff JD, Wang D, Lambros AM, Grinspoon E, Winternitz S, Ren J, Gönenç A, Gruber SA, Ressler KJ, Liu H, Kaufman ML. Large-Scale Functional Brain Network Architecture Changes Associated With Trauma-Related Dissociation. Am J Psychiatry 2021; 178:165-173. [PMID: 32972201 PMCID: PMC8030225 DOI: 10.1176/appi.ajp.2020.19060647] [Citation(s) in RCA: 57] [Impact Index Per Article: 19.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 11/30/2022]
Abstract
OBJECTIVE Dissociative experiences commonly occur in response to trauma, and while their presence strongly affects treatment approaches in posttraumatic spectrum disorders, their etiology remains poorly understood and their phenomenology incompletely characterized. Methods to reliably assess the severity of dissociation symptoms, without relying solely on self-report, would have tremendous clinical utility. Brain-based measures have the potential to augment symptom reports, although it remains unclear whether brain-based measures of dissociation are sufficiently sensitive and robust to enable individual-level estimation of dissociation severity based on brain function. The authors sought to test the robustness and sensitivity of a brain-based measure of dissociation severity. METHODS An intrinsic network connectivity analysis was applied to functional MRI scans obtained from 65 women with histories of childhood abuse and current posttraumatic stress disorder (PTSD). The authors tested for continuous measures of trauma-related dissociation using the Multidimensional Inventory of Dissociation. Connectivity estimates were derived with a novel machine learning technique using individually defined homologous functional regions for each participant. RESULTS The models achieved moderate ability to estimate dissociation, after controlling for childhood trauma and PTSD severity. Connections that contributed the most to the estimation mainly involved the default mode and frontoparietal control networks. By contrast, all models performed at chance levels when using a conventional group-based network parcellation. CONCLUSIONS Trauma-related dissociative symptoms, distinct from PTSD and childhood trauma, can be estimated on the basis of network connectivity. Furthermore, between-network brain connectivity may provide an unbiased estimate of symptom severity, paving the way for more objective, clinically useful biomarkers of dissociation and advancing our understanding of its neural mechanisms.
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Affiliation(s)
- Lauren A M Lebois
- McLean Hospital, Belmont, Mass. (Lebois, Baker, Wolff, Lambros, Grinspoon, Winternitz, Gönenç, Gruber, Ressler, Kaufman); Harvard Medical School, Boston (Lebois, Baker, Winternitz, Gönenç, Gruber, Ressler, Kaufman); Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Harvard Medical School, Charlestown, Mass. (Li, Wang, Ren, Liu); Beijing Institute for Brain Disorders, Capital Medical University, Beijing (Liu); Department of Neuroscience, Medical University of South Carolina, Charleston (Liu)
| | - Meiling Li
- McLean Hospital, Belmont, Mass. (Lebois, Baker, Wolff, Lambros, Grinspoon, Winternitz, Gönenç, Gruber, Ressler, Kaufman); Harvard Medical School, Boston (Lebois, Baker, Winternitz, Gönenç, Gruber, Ressler, Kaufman); Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Harvard Medical School, Charlestown, Mass. (Li, Wang, Ren, Liu); Beijing Institute for Brain Disorders, Capital Medical University, Beijing (Liu); Department of Neuroscience, Medical University of South Carolina, Charleston (Liu)
| | - Justin T Baker
- McLean Hospital, Belmont, Mass. (Lebois, Baker, Wolff, Lambros, Grinspoon, Winternitz, Gönenç, Gruber, Ressler, Kaufman); Harvard Medical School, Boston (Lebois, Baker, Winternitz, Gönenç, Gruber, Ressler, Kaufman); Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Harvard Medical School, Charlestown, Mass. (Li, Wang, Ren, Liu); Beijing Institute for Brain Disorders, Capital Medical University, Beijing (Liu); Department of Neuroscience, Medical University of South Carolina, Charleston (Liu)
| | - Jonathan D Wolff
- McLean Hospital, Belmont, Mass. (Lebois, Baker, Wolff, Lambros, Grinspoon, Winternitz, Gönenç, Gruber, Ressler, Kaufman); Harvard Medical School, Boston (Lebois, Baker, Winternitz, Gönenç, Gruber, Ressler, Kaufman); Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Harvard Medical School, Charlestown, Mass. (Li, Wang, Ren, Liu); Beijing Institute for Brain Disorders, Capital Medical University, Beijing (Liu); Department of Neuroscience, Medical University of South Carolina, Charleston (Liu)
| | - Danhong Wang
- McLean Hospital, Belmont, Mass. (Lebois, Baker, Wolff, Lambros, Grinspoon, Winternitz, Gönenç, Gruber, Ressler, Kaufman); Harvard Medical School, Boston (Lebois, Baker, Winternitz, Gönenç, Gruber, Ressler, Kaufman); Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Harvard Medical School, Charlestown, Mass. (Li, Wang, Ren, Liu); Beijing Institute for Brain Disorders, Capital Medical University, Beijing (Liu); Department of Neuroscience, Medical University of South Carolina, Charleston (Liu)
| | - Ashley M Lambros
- McLean Hospital, Belmont, Mass. (Lebois, Baker, Wolff, Lambros, Grinspoon, Winternitz, Gönenç, Gruber, Ressler, Kaufman); Harvard Medical School, Boston (Lebois, Baker, Winternitz, Gönenç, Gruber, Ressler, Kaufman); Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Harvard Medical School, Charlestown, Mass. (Li, Wang, Ren, Liu); Beijing Institute for Brain Disorders, Capital Medical University, Beijing (Liu); Department of Neuroscience, Medical University of South Carolina, Charleston (Liu)
| | - Elizabeth Grinspoon
- McLean Hospital, Belmont, Mass. (Lebois, Baker, Wolff, Lambros, Grinspoon, Winternitz, Gönenç, Gruber, Ressler, Kaufman); Harvard Medical School, Boston (Lebois, Baker, Winternitz, Gönenç, Gruber, Ressler, Kaufman); Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Harvard Medical School, Charlestown, Mass. (Li, Wang, Ren, Liu); Beijing Institute for Brain Disorders, Capital Medical University, Beijing (Liu); Department of Neuroscience, Medical University of South Carolina, Charleston (Liu)
| | - Sherry Winternitz
- McLean Hospital, Belmont, Mass. (Lebois, Baker, Wolff, Lambros, Grinspoon, Winternitz, Gönenç, Gruber, Ressler, Kaufman); Harvard Medical School, Boston (Lebois, Baker, Winternitz, Gönenç, Gruber, Ressler, Kaufman); Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Harvard Medical School, Charlestown, Mass. (Li, Wang, Ren, Liu); Beijing Institute for Brain Disorders, Capital Medical University, Beijing (Liu); Department of Neuroscience, Medical University of South Carolina, Charleston (Liu)
| | - Jianxun Ren
- McLean Hospital, Belmont, Mass. (Lebois, Baker, Wolff, Lambros, Grinspoon, Winternitz, Gönenç, Gruber, Ressler, Kaufman); Harvard Medical School, Boston (Lebois, Baker, Winternitz, Gönenç, Gruber, Ressler, Kaufman); Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Harvard Medical School, Charlestown, Mass. (Li, Wang, Ren, Liu); Beijing Institute for Brain Disorders, Capital Medical University, Beijing (Liu); Department of Neuroscience, Medical University of South Carolina, Charleston (Liu)
| | - Atilla Gönenç
- McLean Hospital, Belmont, Mass. (Lebois, Baker, Wolff, Lambros, Grinspoon, Winternitz, Gönenç, Gruber, Ressler, Kaufman); Harvard Medical School, Boston (Lebois, Baker, Winternitz, Gönenç, Gruber, Ressler, Kaufman); Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Harvard Medical School, Charlestown, Mass. (Li, Wang, Ren, Liu); Beijing Institute for Brain Disorders, Capital Medical University, Beijing (Liu); Department of Neuroscience, Medical University of South Carolina, Charleston (Liu)
| | - Staci A Gruber
- McLean Hospital, Belmont, Mass. (Lebois, Baker, Wolff, Lambros, Grinspoon, Winternitz, Gönenç, Gruber, Ressler, Kaufman); Harvard Medical School, Boston (Lebois, Baker, Winternitz, Gönenç, Gruber, Ressler, Kaufman); Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Harvard Medical School, Charlestown, Mass. (Li, Wang, Ren, Liu); Beijing Institute for Brain Disorders, Capital Medical University, Beijing (Liu); Department of Neuroscience, Medical University of South Carolina, Charleston (Liu)
| | - Kerry J Ressler
- McLean Hospital, Belmont, Mass. (Lebois, Baker, Wolff, Lambros, Grinspoon, Winternitz, Gönenç, Gruber, Ressler, Kaufman); Harvard Medical School, Boston (Lebois, Baker, Winternitz, Gönenç, Gruber, Ressler, Kaufman); Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Harvard Medical School, Charlestown, Mass. (Li, Wang, Ren, Liu); Beijing Institute for Brain Disorders, Capital Medical University, Beijing (Liu); Department of Neuroscience, Medical University of South Carolina, Charleston (Liu)
| | - Hesheng Liu
- McLean Hospital, Belmont, Mass. (Lebois, Baker, Wolff, Lambros, Grinspoon, Winternitz, Gönenç, Gruber, Ressler, Kaufman); Harvard Medical School, Boston (Lebois, Baker, Winternitz, Gönenç, Gruber, Ressler, Kaufman); Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Harvard Medical School, Charlestown, Mass. (Li, Wang, Ren, Liu); Beijing Institute for Brain Disorders, Capital Medical University, Beijing (Liu); Department of Neuroscience, Medical University of South Carolina, Charleston (Liu)
| | - Milissa L Kaufman
- McLean Hospital, Belmont, Mass. (Lebois, Baker, Wolff, Lambros, Grinspoon, Winternitz, Gönenç, Gruber, Ressler, Kaufman); Harvard Medical School, Boston (Lebois, Baker, Winternitz, Gönenç, Gruber, Ressler, Kaufman); Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Harvard Medical School, Charlestown, Mass. (Li, Wang, Ren, Liu); Beijing Institute for Brain Disorders, Capital Medical University, Beijing (Liu); Department of Neuroscience, Medical University of South Carolina, Charleston (Liu)
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9
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Kashyap R, Eng GK, Bhattacharjee S, Gupta B, Ho R, Ho CSH, Zhang M, Mahendran R, Sim K, Chen SHA. Individual-fMRI-approaches reveal cerebellum and visual communities to be functionally connected in obsessive compulsive disorder. Sci Rep 2021; 11:1354. [PMID: 33446780 PMCID: PMC7809273 DOI: 10.1038/s41598-020-80346-6] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/04/2020] [Accepted: 12/11/2020] [Indexed: 01/29/2023] Open
Abstract
There is significant interest in understanding the pathophysiology of Obsessive-Compulsive Disorder (OCD) using resting-state fMRI (rsfMRI). Previous studies acknowledge abnormalities within and beyond the fronto-striato-limbic circuit in OCD that require further clarifications. However, limited information could be inferred from the conventional way of investigating the functional connectivity differences between OCD and healthy controls. Here, we identified altered brain organization in patients with OCD by applying individual-based approaches to maximize the identification of underlying network-based features specific to the OCD group. rsfMRI of 20 patients with OCD and 22 controls were preprocessed, and individual-fMRI-subspace was derived for each subject within each group. We evaluated group differences in functional connectivity using individual-fMRI-subspace and established its advantage over conventional-fMRI methodology. We applied prediction-based approaches to highlight the group differences by evaluating the differences in functional connections that predicted the clinical scores (namely, the Obsessive-Compulsive Inventory-Revised (OCI-R) and Hamilton Anxiety Rating Scale). Then, we explored the brain network organization of both groups by estimating the subject-specific communities within each group. Lastly, we evaluated associations between the inter-individual variation of nodes in the communities to clinical measures using linear regression. Functional connectivity analysis using individual-fMRI-subspace detected 83 connections that were different between OCD and control groups, compared to none found using conventional-fMRI methodology. Connectome-based prediction analysis did not show significant overlap between the two groups in the functional connections that predicted the clinical scores. This suggests that the functional architecture in patients with OCD may be different compared to controls. Seven communities were found in both groups. Interestingly, within the OCD group but not controls, we observed functional connectivity between cerebellar and visual regions, and lack of connectivity between striato-limbic and frontal areas. Inter-individual variations in the community-size of these two communities were also associated with the OCI-R score (p < .005). Due to our small sample size, we further validated our results by (i) accounting for head motion, (ii) applying global signal regression (GSR) in data processing, and (iii) using an alternate atlas for parcellation. While the main results were consistently observed with accounting for head motion and using another atlas, the key findings were not reproduced with GSR application. The study demonstrated the existence of disconnectedness in fronto-striato-limbic community and connectedness between cerebellar and visual areas in OCD patients, which was also related to the clinical symptomatology of OCD.
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Affiliation(s)
- Rajan Kashyap
- Centre for Research and Development in Learning (CRADLE), Nanyang Technological University, CRADLE, 61 Nanyang Drive, ABN-01b-10, Singapore, 637335, Singapore.
| | - Goi Khia Eng
- Department of Psychiatry, New York University School of Medicine, New York, USA
- Division of Clinical Research, Nathan S. Kline Institute for Psychiatric Research, Orangeburg, USA
- School of Social Sciences (SSS), Nanyang Technological University, 48 Nanyang Ave, SHHK-04-19, Singapore, 639818, Singapore
| | - Sagarika Bhattacharjee
- School of Social Sciences (SSS), Nanyang Technological University, 48 Nanyang Ave, SHHK-04-19, Singapore, 639818, Singapore
| | - Bhanu Gupta
- Community Psychiatry, Institute of Mental Health, Singapore, Singapore
| | - Roger Ho
- Psychological Medicine, National University Health Systems, Singapore, Singapore
| | - Cyrus S H Ho
- Psychological Medicine, National University Health Systems, Singapore, Singapore
| | - Melvyn Zhang
- Psychological Medicine, National University Health Systems, Singapore, Singapore
| | - Rathi Mahendran
- Psychological Medicine, National University Health Systems, Singapore, Singapore
- Academic Development Department, Duke-NUS Medical School, Singapore, Singapore
| | - Kang Sim
- West Region, Institute of Mental Health, Singapore, Singapore
| | - S H Annabel Chen
- Centre for Research and Development in Learning (CRADLE), Nanyang Technological University, CRADLE, 61 Nanyang Drive, ABN-01b-10, Singapore, 637335, Singapore.
- School of Social Sciences (SSS), Nanyang Technological University, 48 Nanyang Ave, SHHK-04-19, Singapore, 639818, Singapore.
- Lee Kong Chian School of Medicine (LKC Medicine), Nanyang Technological University, Singapore, Singapore.
- Office of Educational Research, National Institute of Education, Nanyang Technological University, Singapore, Singapore.
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10
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Fan Y, Li L, Peng Y, Li H, Guo J, Li M, Yang S, Yao M, Zhao J, Liu H, Liao W, Guo X, Han S, Cui Q, Duan X, Xu Y, Zhang Y, Chen H. Individual-specific functional connectome biomarkers predict schizophrenia positive symptoms during adolescent brain maturation. Hum Brain Mapp 2020; 42:1475-1484. [PMID: 33289223 PMCID: PMC7927287 DOI: 10.1002/hbm.25307] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/29/2020] [Revised: 11/09/2020] [Accepted: 11/23/2020] [Indexed: 11/06/2022] Open
Abstract
Even with an overarching functional dysconnectivity model of adolescent-onset schizophrenia (AOS), there have been no functional connectome (FC) biomarkers identified for predicting patients' specific symptom domains. Adolescence is a period of dramatic brain maturation, with substantial interindividual variability in brain anatomy. However, existing group-level hypotheses of AOS lack precision in terms of neuroanatomical boundaries. This study aimed to identify individual-specific FC biomarkers associated with schizophrenic symptom manifestation during adolescent brain maturation. We used a reliable individual-level cortical parcellation approach to map functional brain regions in each subject, that were then used to identify FC biomarkers for predicting dimension-specific psychotic symptoms in 30 antipsychotic-naïve first-episode AOS patients (recruited sample of 39). Age-related changes in biomarker expression were compared between these patients and 31 healthy controls. Moreover, 29 antipsychotic-naïve first-episode AOS patients (analyzed sample of 25) were recruited from another center to test the generalizability of the prediction model. Individual-specific FC biomarkers could significantly and better predict AOS positive-dimension symptoms with a relatively stronger generalizability than at the group level. Specifically, positive symptom domains were estimated based on connections between the frontoparietal control network (FPN) and salience network and within FPN. Consistent with the neurodevelopmental hypothesis of schizophrenia, the FPN-SN connection exhibited aberrant age-associated alteration in AOS. The individual-level findings reveal reproducible FPN-based FC biomarkers associated with AOS positive symptom domains, and highlight the importance of accounting for individual variation in the study of adolescent-onset disorders.
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Affiliation(s)
- Yun‐Shuang Fan
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Lab for Neuroinformation, School of life Science and technologyUniversity of Electronic Science and Technology of ChinaChengduChina
| | - Liang Li
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Lab for Neuroinformation, School of life Science and technologyUniversity of Electronic Science and Technology of ChinaChengduChina
| | - Yue Peng
- Department of PsychiatryThe Second Affiliated Hospital of Xinxiang Medical UniversityXinxiangChina
| | - Haoru Li
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Lab for Neuroinformation, School of life Science and technologyUniversity of Electronic Science and Technology of ChinaChengduChina
| | - Jing Guo
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Lab for Neuroinformation, School of life Science and technologyUniversity of Electronic Science and Technology of ChinaChengduChina
| | - Meiling Li
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Lab for Neuroinformation, School of life Science and technologyUniversity of Electronic Science and Technology of ChinaChengduChina
- Athinoula A. Martinos Center for Biomedical Imaging, Department of RadiologyMassachusetts General Hospital, Harvard Medical SchoolCharlestownMassachusettsUSA
| | - Siqi Yang
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Lab for Neuroinformation, School of life Science and technologyUniversity of Electronic Science and Technology of ChinaChengduChina
| | - Meng Yao
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Lab for Neuroinformation, School of life Science and technologyUniversity of Electronic Science and Technology of ChinaChengduChina
| | - Jingping Zhao
- Institute of Mental HealthThe Second Xiangya Hospital, Central South UniversityChangshaChina
| | - Hesheng Liu
- Athinoula A. Martinos Center for Biomedical Imaging, Department of RadiologyMassachusetts General Hospital, Harvard Medical SchoolCharlestownMassachusettsUSA
| | - Wei Liao
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Lab for Neuroinformation, School of life Science and technologyUniversity of Electronic Science and Technology of ChinaChengduChina
| | - Xiaonan Guo
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Lab for Neuroinformation, School of life Science and technologyUniversity of Electronic Science and Technology of ChinaChengduChina
| | - Shaoqiang Han
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Lab for Neuroinformation, School of life Science and technologyUniversity of Electronic Science and Technology of ChinaChengduChina
| | - Qian Cui
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Lab for Neuroinformation, School of life Science and technologyUniversity of Electronic Science and Technology of ChinaChengduChina
| | - Xujun Duan
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Lab for Neuroinformation, School of life Science and technologyUniversity of Electronic Science and Technology of ChinaChengduChina
| | - Yong Xu
- Department of PsychiatryFirst Hospital/First Clinical Medical College of Shanxi Medical UniversityTaiyuanChina
| | - Yan Zhang
- Department of PsychiatryThe Second Affiliated Hospital of Xinxiang Medical UniversityXinxiangChina
| | - Huafu Chen
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Lab for Neuroinformation, School of life Science and technologyUniversity of Electronic Science and Technology of ChinaChengduChina
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11
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Sui J, Jiang R, Bustillo J, Calhoun V. Neuroimaging-based Individualized Prediction of Cognition and Behavior for Mental Disorders and Health: Methods and Promises. Biol Psychiatry 2020; 88:818-828. [PMID: 32336400 PMCID: PMC7483317 DOI: 10.1016/j.biopsych.2020.02.016] [Citation(s) in RCA: 140] [Impact Index Per Article: 35.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/13/2019] [Revised: 02/13/2020] [Accepted: 02/17/2020] [Indexed: 01/08/2023]
Abstract
The neuroimaging community has witnessed a paradigm shift in biomarker discovery from using traditional univariate brain mapping approaches to multivariate predictive models, allowing the field to move toward a translational neuroscience era. Regression-based multivariate models (hereafter "predictive modeling") provide a powerful and widely used approach to predict human behavior with neuroimaging features. These studies maintain a focus on decoding individual differences in a continuously behavioral phenotype from neuroimaging data, opening up an exciting opportunity to describe the human brain at the single-subject level. In this survey, we provide an overview of recent studies that utilize machine learning approaches to identify neuroimaging predictors over the past decade. We first review regression-based approaches and highlight connectome-based predictive modeling, which has grown in popularity in recent years. Next, we systematically describe recent representative studies using these tools in the context of cognitive function, symptom severity, personality traits, and emotion processing. Finally, we highlight a few challenges related to combining multimodal data, longitudinal prediction, external validations, and the employment of deep learning methods that have emerged from our review of the existing literature, as well as present some promising and challenging future directions.
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Affiliation(s)
- Jing Sui
- Brainnetome Center and National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, Beijing, China; School of Artificial Intelligence, University of Chinese Academy of Sciences, Beijing, China; Chinese Academy of Sciences Center for Excellence in Brain Science and Intelligence Technology, Institute of Automation, Chinese Academy of Sciences, Beijing, China; Tri-institutional Center for Translational Research in Neuroimaging and Data Science, Georgia State University, Georgia Institute of Technology, and Emory University, Atlanta, Georgia.
| | - Rongtao Jiang
- Brainnetome Center and National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, Beijing, China; School of Artificial Intelligence, University of Chinese Academy of Sciences, Beijing, China
| | - Juan Bustillo
- Department of Psychiatry, University of New Mexico, Albuquerque, New Mexico
| | - Vince Calhoun
- Tri-institutional Center for Translational Research in Neuroimaging and Data Science, Georgia State University, Georgia Institute of Technology, and Emory University, Atlanta, Georgia.
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Kwak S, Kim M, Kim T, Kwak Y, Oh S, Lho SK, Moon SY, Lee TY, Kwon JS. Defining data-driven subgroups of obsessive-compulsive disorder with different treatment responses based on resting-state functional connectivity. Transl Psychiatry 2020; 10:359. [PMID: 33106472 PMCID: PMC7589530 DOI: 10.1038/s41398-020-01045-4] [Citation(s) in RCA: 12] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/07/2020] [Revised: 09/07/2020] [Accepted: 09/09/2020] [Indexed: 12/18/2022] Open
Abstract
Characterization of obsessive-compulsive disorder (OCD), like other psychiatric disorders, suffers from heterogeneities in its symptoms and therapeutic responses, and identification of more homogeneous subgroups may help to resolve the heterogeneity. We aimed to identify the OCD subgroups based on resting-state functional connectivity (rsFC) and to explore their differences in treatment responses via a multivariate approach. From the resting-state functional MRI data of 107 medication-free OCD patients and 110 healthy controls (HCs), we selected rsFC features, which discriminated OCD patients from HCs via support vector machine (SVM) analyses. With the selected brain features, we subdivided OCD patients into subgroups using hierarchical clustering analyses. We identified 35 rsFC features that achieved a high sensitivity (82.74%) and specificity (76.29%) in SVM analyses. The OCD patients were subdivided into two subgroups, which did not show significant differences in their demographic and clinical backgrounds. However, one of the OCD subgroups demonstrated more impaired rsFC that was involved either within the default mode network (DMN) or between DMN brain regions and other network regions. This subgroup also showed both lower improvements in symptom severity in the 16-week follow-up visit and lower responder percentage than the other subgroup. Our results highlight that not only abnormalities within the DMN but also aberrant rsFC between the DMN and other networks may contribute to the treatment response and support the importance of these neurobiological alterations in OCD patients. We suggest that abnormalities in these connectivity may play predictive biomarkers of treatment response, and aid to build more optimal treatment strategies.
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Affiliation(s)
- Seoyeon Kwak
- Department of Brain and Cognitive Sciences, Seoul National University College of Natural Sciences, Seoul, Republic of Korea
| | - Minah Kim
- Department of Neuropsychiatry, Seoul National University Hospital, Seoul, Republic of Korea
- Department of Psychiatry, Seoul National University College of Medicine, Seoul, Republic of Korea
| | - Taekwan Kim
- Department of Brain and Cognitive Sciences, Seoul National University College of Natural Sciences, Seoul, Republic of Korea
| | - Yoobin Kwak
- Department of Brain and Cognitive Sciences, Seoul National University College of Natural Sciences, Seoul, Republic of Korea
| | - Sanghoon Oh
- Department of Neuropsychiatry, Seoul National University Hospital, Seoul, Republic of Korea
- Department of Psychiatry, Seoul National University College of Medicine, Seoul, Republic of Korea
| | - Silvia Kyungjin Lho
- Department of Neuropsychiatry, Seoul National University Hospital, Seoul, Republic of Korea
- Department of Psychiatry, Seoul National University College of Medicine, Seoul, Republic of Korea
| | - Sun-Young Moon
- Department of Neuropsychiatry, Seoul National University Hospital, Seoul, Republic of Korea
- Department of Psychiatry, Seoul National University College of Medicine, Seoul, Republic of Korea
| | - Tae Young Lee
- Department of Neuropsychiatry, Pusan National University Yangsan Hospital, Yangsan, Republic of Korea
- Institute of Human Behavioral Medicine, SNU-MRC, Seoul, Republic of Korea
| | - Jun Soo Kwon
- Department of Brain and Cognitive Sciences, Seoul National University College of Natural Sciences, Seoul, Republic of Korea.
- Department of Neuropsychiatry, Seoul National University Hospital, Seoul, Republic of Korea.
- Department of Psychiatry, Seoul National University College of Medicine, Seoul, Republic of Korea.
- Institute of Human Behavioral Medicine, SNU-MRC, Seoul, Republic of Korea.
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13
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Szechtman H, Harvey BH, Woody EZ, Hoffman KL. The Psychopharmacology of Obsessive-Compulsive Disorder: A Preclinical Roadmap. Pharmacol Rev 2020; 72:80-151. [PMID: 31826934 DOI: 10.1124/pr.119.017772] [Citation(s) in RCA: 24] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/11/2022] Open
Abstract
This review evaluates current knowledge about obsessive-compulsive disorder (OCD), with the goal of providing a roadmap for future directions in research on the psychopharmacology of the disorder. It first addresses issues in the description and diagnosis of OCD, including the structure, measurement, and appropriate description of the disorder and issues of differential diagnosis. Current pharmacotherapies for OCD are then reviewed, including monotherapy with serotonin reuptake inhibitors and augmentation with antipsychotic medication and with psychologic treatment. Neuromodulatory therapies for OCD are also described, including psychosurgery, deep brain stimulation, and noninvasive brain stimulation. Psychotherapies for OCD are then reviewed, focusing on behavior therapy, including exposure and response prevention and cognitive therapy, and the efficacy of these interventions is discussed, touching on issues such as the timing of sessions, the adjunctive role of pharmacotherapy, and the underlying mechanisms. Next, current research on the neurobiology of OCD is examined, including work probing the role of various neurotransmitters and other endogenous processes and etiology as clues to the neurobiological fault that may underlie OCD. A new perspective on preclinical research is advanced, using the Research Domain Criteria to propose an adaptationist viewpoint that regards OCD as the dysfunction of a normal motivational system. A systems-design approach introduces the security motivation system (SMS) theory of OCD as a framework for research. Finally, a new perspective on psychopharmacological research for OCD is advanced, exploring three approaches: boosting infrastructure facilities of the brain, facilitating psychotherapeutic relearning, and targeting specific pathways of the SMS network to fix deficient SMS shut-down processes. SIGNIFICANCE STATEMENT: A significant proportion of patients with obsessive-compulsive disorder (OCD) do not achieve remission with current treatments, indicating the need for innovations in psychopharmacology for the disorder. OCD may be conceptualized as the dysfunction of a normal, special motivation system that evolved to manage the prospect of potential danger. This perspective, together with a wide-ranging review of the literature, suggests novel directions for psychopharmacological research, including boosting support systems of the brain, facilitating relearning that occurs in psychotherapy, and targeting specific pathways in the brain that provide deficient stopping processes in OCD.
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Affiliation(s)
- Henry Szechtman
- Department of Psychiatry and Behavioural Neurosciences, McMaster University, Hamilton, Ontario, Canada (H.S.); SAMRC Unit on Risk Resilience in Mental Disorders, Department of Psychiatry, University of Cape Town, and Center of Excellence for Pharmaceutical Sciences, School of Pharmacy, North-West University (Potchefstroom Campus), Potchefstroom, South Africa (B.H.H.); Department of Psychology, University of Waterloo, Waterloo, Ontario, Canada (E.Z.W.); and Centro de Investigación en Reproducción Animal, CINVESTAV-Universidad Autónoma de Tlaxcala, Tlaxcala, Mexico (K.L.H.)
| | - Brian H Harvey
- Department of Psychiatry and Behavioural Neurosciences, McMaster University, Hamilton, Ontario, Canada (H.S.); SAMRC Unit on Risk Resilience in Mental Disorders, Department of Psychiatry, University of Cape Town, and Center of Excellence for Pharmaceutical Sciences, School of Pharmacy, North-West University (Potchefstroom Campus), Potchefstroom, South Africa (B.H.H.); Department of Psychology, University of Waterloo, Waterloo, Ontario, Canada (E.Z.W.); and Centro de Investigación en Reproducción Animal, CINVESTAV-Universidad Autónoma de Tlaxcala, Tlaxcala, Mexico (K.L.H.)
| | - Erik Z Woody
- Department of Psychiatry and Behavioural Neurosciences, McMaster University, Hamilton, Ontario, Canada (H.S.); SAMRC Unit on Risk Resilience in Mental Disorders, Department of Psychiatry, University of Cape Town, and Center of Excellence for Pharmaceutical Sciences, School of Pharmacy, North-West University (Potchefstroom Campus), Potchefstroom, South Africa (B.H.H.); Department of Psychology, University of Waterloo, Waterloo, Ontario, Canada (E.Z.W.); and Centro de Investigación en Reproducción Animal, CINVESTAV-Universidad Autónoma de Tlaxcala, Tlaxcala, Mexico (K.L.H.)
| | - Kurt Leroy Hoffman
- Department of Psychiatry and Behavioural Neurosciences, McMaster University, Hamilton, Ontario, Canada (H.S.); SAMRC Unit on Risk Resilience in Mental Disorders, Department of Psychiatry, University of Cape Town, and Center of Excellence for Pharmaceutical Sciences, School of Pharmacy, North-West University (Potchefstroom Campus), Potchefstroom, South Africa (B.H.H.); Department of Psychology, University of Waterloo, Waterloo, Ontario, Canada (E.Z.W.); and Centro de Investigación en Reproducción Animal, CINVESTAV-Universidad Autónoma de Tlaxcala, Tlaxcala, Mexico (K.L.H.)
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14
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Karcher NR, Michelini G, Kotov R, Barch DM. Associations Between Resting-State Functional Connectivity and a Hierarchical Dimensional Structure of Psychopathology in Middle Childhood. BIOLOGICAL PSYCHIATRY: COGNITIVE NEUROSCIENCE AND NEUROIMAGING 2020; 6:508-517. [PMID: 33229246 DOI: 10.1016/j.bpsc.2020.09.008] [Citation(s) in RCA: 16] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/15/2020] [Revised: 08/11/2020] [Accepted: 09/14/2020] [Indexed: 12/31/2022]
Abstract
BACKGROUND Previous research from the Adolescent Brain Cognitive Development (ABCD) Study delineated and validated a hierarchical 5-factor structure with a general psychopathology (p) factor at the apex and 5 specific factors (internalizing, somatoform, detachment, neurodevelopmental, externalizing) using parent-reported child symptoms. The present study is the first to examine associations between dimensions from a hierarchical structure and resting-state functional connectivity (RSFC) networks. METHODS Using 9- to 11-year-old children from the ABCD Study baseline sample, we examined the variance explained by each hierarchical structure level (p-factor, 2-factor, 3-factor, 4-factor, and 5-factor models) in associations with RSFC. Analyses were first conducted in a discovery dataset (n = 3790), and significant associations were examined in a replication dataset (n = 3791). RESULTS There were robust associations between the p-factor and lower connectivity within the default mode network, although stronger effects emerged for the neurodevelopmental factor. Neurodevelopmental impairments were also related to variation in RSFC networks associated with attention to internal states and external stimuli. Analyses revealed robust associations between the neurodevelopmental dimension and several RSFC metrics, including within the default mode network, between the default mode network with cingulo-opercular and "Other" (unassigned) networks, and between the dorsal attention network with the Other network. CONCLUSIONS The hierarchical structure of psychopathology showed replicable links to RSFC associations in middle childhood. The specific neurodevelopmental dimension showed robust associations with multiple RSFC metrics. These results show the utility of examining associations between intrinsic brain architecture and specific dimensions of psychopathology, revealing associations especially with neurodevelopmental impairments.
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Affiliation(s)
- Nicole R Karcher
- Department of Psychiatry, Washington University School of Medicine, St. Louis, Missouri.
| | - Giorgia Michelini
- Semel Institute for Neuroscience and Human Behavior, University of California, Los Angeles, Los Angeles, California
| | - Roman Kotov
- Department of Psychiatry and Behavioral Health, Stony Brook University, Stony Brook, New York
| | - Deanna M Barch
- Department of Psychiatry, Washington University School of Medicine, St. Louis, Missouri; Department of Psychology, Washington University, St. Louis, Missouri
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15
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Gratton C, Kraus BT, Greene DJ, Gordon EM, Laumann TO, Nelson SM, Dosenbach NUF, Petersen SE. Defining Individual-Specific Functional Neuroanatomy for Precision Psychiatry. Biol Psychiatry 2020; 88:28-39. [PMID: 31916942 PMCID: PMC7203002 DOI: 10.1016/j.biopsych.2019.10.026] [Citation(s) in RCA: 89] [Impact Index Per Article: 22.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/10/2019] [Revised: 10/07/2019] [Accepted: 10/25/2019] [Indexed: 12/28/2022]
Abstract
Studies comparing diverse groups have shown that many psychiatric diseases involve disruptions across distributed large-scale networks of the brain. There is hope that functional magnetic resonance imaging (fMRI) functional connectivity techniques will shed light on these disruptions, providing prognostic and diagnostic biomarkers as well as targets for therapeutic interventions. However, to date, progress on clinical translation of fMRI methods has been limited. Here, we argue that this limited translation is driven by a combination of intersubject heterogeneity and the relatively low reliability of standard fMRI techniques at the individual level. We review a potential solution to these limitations: the use of new "precision" fMRI approaches that shift the focus of analysis from groups to single individuals through the use of extended data acquisition strategies. We begin by discussing the potential advantages of fMRI functional connectivity methods for improving our understanding of functional neuroanatomy and disruptions in psychiatric disorders. We then discuss the budding field of precision fMRI and findings garnered from this work. We demonstrate that precision fMRI can improve the reliability of functional connectivity measures, while showing high stability and sensitivity to individual differences. We close by discussing the application of these approaches to clinical settings.
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Affiliation(s)
- Caterina Gratton
- Department of Psychology, Northwestern University, Evanston, Illinois; Department of Neurology, Northwestern University, Evanston, Illinois.
| | - Brian T Kraus
- Department of Psychology, Northwestern University, Evanston, Illinois
| | - Deanna J Greene
- Department of Psychiatry, Washington University in St. Louis, St. Louis, Missouri; Department of Radiology, Washington University in St. Louis, St. Louis, Missouri
| | - Evan M Gordon
- VISN Center of Excellence for Research on Returning War Veterans, Waco, Texas; Department of Psychology and Neuroscience, Baylor University, Waco, Texas; Center for Vital Longevity, School of Behavioral and Brain Sciences, University of Texas at Dallas, Dallas, Texas
| | - Timothy O Laumann
- Department of Psychiatry, Washington University in St. Louis, St. Louis, Missouri
| | - Steven M Nelson
- VISN Center of Excellence for Research on Returning War Veterans, Waco, Texas; Department of Psychology and Neuroscience, Baylor University, Waco, Texas; Center for Vital Longevity, School of Behavioral and Brain Sciences, University of Texas at Dallas, Dallas, Texas; Department of Psychiatry and Behavioral Science, Texas A&M Health Science Center, College of Medicine, Bryan, Texas
| | - Nico U F Dosenbach
- Department of Radiology, Washington University in St. Louis, St. Louis, Missouri; Department of Neurology, Washington University in St. Louis, St. Louis, Missouri; Department of Pediatrics, Washington University in St. Louis, St. Louis, Missouri; Department of Biomedical Engineering, Washington University in St. Louis, St. Louis, Missouri
| | - Steven E Petersen
- Department of Psychiatry, Washington University in St. Louis, St. Louis, Missouri; Department of Radiology, Washington University in St. Louis, St. Louis, Missouri; Department of Neurology, Washington University in St. Louis, St. Louis, Missouri; Department of Neuroscience, Washington University in St. Louis, St. Louis, Missouri; Department of Psychological and Brain Sciences, Washington University in St. Louis, St. Louis, Missouri
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16
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Bas-Hoogendam JM, Westenberg PM. Imaging the socially-anxious brain: recent advances and future prospects. F1000Res 2020; 9:F1000 Faculty Rev-230. [PMID: 32269760 PMCID: PMC7122428 DOI: 10.12688/f1000research.21214.1] [Citation(s) in RCA: 9] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Accepted: 03/23/2020] [Indexed: 12/20/2022] Open
Abstract
Social anxiety disorder (SAD) is serious psychiatric condition with a genetic background. Insight into the neurobiological alterations underlying the disorder is essential to develop effective interventions that could relieve SAD-related suffering. In this expert review, we consider recent neuroimaging work on SAD. First, we focus on new results from magnetic resonance imaging studies dedicated to outlining biomarkers of SAD, including encouraging findings with respect to structural and functional brain alterations associated with the disorder. Furthermore, we highlight innovative studies in the field of neuroprediction and studies that established the effects of treatment on brain characteristics. Next, we describe novel work aimed to delineate endophenotypes of SAD, providing insight into the genetic susceptibility to develop the disorder. Finally, we outline outstanding questions and point out directions for future research.
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Affiliation(s)
- Janna Marie Bas-Hoogendam
- Developmental and Educational Psychology, Institute of Psychology, Leiden University, Wassenaarseweg 52, 2333 AK Leiden, The Netherlands
- Leiden Institute for Brain and Cognition, c/o LUMC, postzone C2-S, P.O.Box 9600, 2300 RC Leiden, The Netherlands
- Department of Psychiatry, Leiden University Medical Center, Albinusdreef 2, 2333 ZA Leiden, The Netherlands
| | - P. Michiel Westenberg
- Developmental and Educational Psychology, Institute of Psychology, Leiden University, Wassenaarseweg 52, 2333 AK Leiden, The Netherlands
- Leiden Institute for Brain and Cognition, c/o LUMC, postzone C2-S, P.O.Box 9600, 2300 RC Leiden, The Netherlands
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17
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Chalah MA, Ayache SS. Could Transcranial Direct Current Stimulation Join the Therapeutic Armamentarium in Obsessive-Compulsive Disorder? Brain Sci 2020; 10:brainsci10020125. [PMID: 32102163 PMCID: PMC7071454 DOI: 10.3390/brainsci10020125] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/16/2020] [Accepted: 02/21/2020] [Indexed: 12/14/2022] Open
Abstract
Obsessive-compulsive disorder (OCD) is a mental disorder that can affect around 1-3% of individuals [...].
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Affiliation(s)
- Moussa A. Chalah
- Service de Physiologie, Explorations Fonctionnelles, Hôpital Henri-Mondor, AP-HP, 94010 Créteil, France;
- EA 4391, Excitabilité Nerveuse et Thérapeutique, Université Paris-Est-Créteil, 94010 Créteil, France
| | - Samar S. Ayache
- Service de Physiologie, Explorations Fonctionnelles, Hôpital Henri-Mondor, AP-HP, 94010 Créteil, France;
- EA 4391, Excitabilité Nerveuse et Thérapeutique, Université Paris-Est-Créteil, 94010 Créteil, France
- Correspondence:
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18
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Abstract
In neuroimaging research, averaging data at the level of the group results in blurring of potentially meaningful individual differences. A more widespread use of an individual-specific approach is advocated for, which involves a more thorough investigation of each individual in a group, and characterization of idiosyncrasies at the level of behavior, cognition, and symptoms, as well as at the level of brain organization. It is hoped that such an approach, focused on individuals, will provide convergent findings that will help identify the underlying pathologic condition in various psychiatric disorders and help in the development of treatments individualized for each patient.
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Affiliation(s)
- Hesheng Liu
- Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Harvard Medical School, 149 13th Street, Suite 2301, Charlestown, MA 02129, USA.
| | - William J Liu
- Department of Neuroscience, Grossman Institute of Neurobiology, The College, University of Chicago, 5812 South Ellis Avenue, MC 0912, Suite P-400, Chicago, IL 60637, USA
| | - Danhong Wang
- Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Harvard Medical School, 149 13th Street, Suite 2301, Charlestown, MA 02129, USA
| | - Louisa Dahmani
- Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Harvard Medical School, 149 13th Street, Suite 2301, Charlestown, MA 02129, USA
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19
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Li M, Wang D, Ren J, Langs G, Stoecklein S, Brennan BP, Lu J, Chen H, Liu H. Performing group-level functional image analyses based on homologous functional regions mapped in individuals. PLoS Biol 2019; 17:e2007032. [PMID: 30908490 PMCID: PMC6448916 DOI: 10.1371/journal.pbio.2007032] [Citation(s) in RCA: 67] [Impact Index Per Article: 13.4] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/13/2018] [Revised: 04/04/2019] [Accepted: 03/05/2019] [Indexed: 12/13/2022] Open
Abstract
Functional MRI (fMRI) studies have traditionally relied on intersubject normalization based on global brain morphology, which cannot establish proper functional correspondence between subjects due to substantial intersubject variability in functional organization. Here, we reliably identified a set of discrete, homologous functional regions in individuals to improve intersubject alignment of fMRI data. These functional regions demonstrated marked intersubject variability in size, position, and connectivity. We found that previously reported intersubject variability in functional connectivity maps could be partially explained by variability in size and position of the functional regions. Importantly, individual differences in network topography are associated with individual differences in task-evoked activations, suggesting that these individually specified regions may serve as the "localizer" to improve the alignment of task-fMRI data. We demonstrated that aligning task-fMRI data using the regions derived from resting state fMRI may lead to increased statistical power of task-fMRI analyses. In addition, resting state functional connectivity among these homologous regions is able to capture the idiosyncrasies of subjects and better predict fluid intelligence (gF) than connectivity measures derived from group-level brain atlases. Critically, we showed that not only the connectivity but also the size and position of functional regions are related to human behavior. Collectively, these findings suggest that identifying homologous functional regions across individuals can benefit a wide range of studies in the investigation of connectivity, task activation, and brain-behavior associations.
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Affiliation(s)
- Meiling Li
- Key Laboratory for NeuroInformation of Ministry of Education, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, China
- Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Harvard Medical School, Charlestown, Massachusetts, United States of America
| | - Danhong Wang
- Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Harvard Medical School, Charlestown, Massachusetts, United States of America
| | - Jianxun Ren
- Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Harvard Medical School, Charlestown, Massachusetts, United States of America
- National Engineering Laboratory for Neuromodulation, School of Aerospace Engineering, Tsinghua University, Beijing, China
| | - Georg Langs
- Department of Biomedical Imaging and Image-guided Therapy, Computational Imaging Research Lab, Medical University of Vienna, Vienna, Austria
| | - Sophia Stoecklein
- Institute of Clinical Radiology, Ludwig-Maximilians University of Munich, Munich Germany
| | - Brian P. Brennan
- McLean Hospital, Harvard Medical School, Belmont, Massachusetts, United States of America
| | - Jie Lu
- Department of Radiology, Xuanwu Hospital, Beijing, China
| | - Huafu Chen
- Key Laboratory for NeuroInformation of Ministry of Education, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, China
| | - Hesheng Liu
- Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Harvard Medical School, Charlestown, Massachusetts, United States of America
- Beijing Institute for Brain Disorders, Capital Medical University, Beijing, China
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20
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Hoexter MQ. Are We Ready for Individualized Target Planning of Ablative Procedures in Intractable Obsessive-Compulsive Disorder? Biol Psychiatry 2018; 84:e85-e87. [PMID: 30466508 DOI: 10.1016/j.biopsych.2018.10.014] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/09/2018] [Accepted: 10/17/2018] [Indexed: 02/07/2023]
Affiliation(s)
- Marcelo Q Hoexter
- Department and Institute of Psychiatry, Hospital das Clínicas, University of São Paulo School of Medicine, São Paulo, Brazil.
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