1
|
Yan H, Shan X, Li H, Liu F, Xie G, Li P, Guo W. Cerebellar functional connectivity and its associated genes: A longitudinal study in drug-naive patients with obsessive-compulsive disorder. J Psychiatr Res 2024; 177:378-391. [PMID: 39083996 DOI: 10.1016/j.jpsychires.2024.07.040] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/12/2024] [Revised: 07/19/2024] [Accepted: 07/27/2024] [Indexed: 08/02/2024]
Abstract
The role of cerebellar-cerebral functional connectivity (CC-FC) in obsessive-compulsive disorder (OCD), its trajectory post-pharmacotherapy, and its potential as a prognostic biomarker and genetic mechanism remain uncertain. To address these gaps, this study included 37 drug-naive OCD patients and 37 healthy controls (HCs). Participants underwent baseline functional magnetic resonance imaging (fMRI), followed by four weeks of paroxetine treatment for patients with OCD, and another fMRI scan post-treatment. We examined seed-based CC-FC differences between the patients and HCs, and pre- and post-treatment patients. Support vector regression (SVR) based on CC-FC was performed to predict treatment response. Correlation analysis explored associations between CC-FC and clinical features, as well as gene profiles. Compared to HCs, drug-naive OCD patients exhibited reduced CC-FC in executive, affective-limbic, and sensorimotor networks, with specific genetic profiles associated with altered CC-FC. Gene enrichment analyses highlighted the involvement of these genes in various biological processes, molecular functions, and pathways. Post-treatment, the patients showed partial clinical improvement and partial restoration of the previously decreased CC-FC. Abnormal CC-FC at baseline correlated negatively with compulsions severity and social functional impairment, while changes in CC-FC correlated with cognitive function changes post-treatment. CC-FC emerged as a potential predictor of symptom severity in patients following paroxetine treatment. This longitudinal resting-state fMRI study underscores the crucial role of CC-FC in the neuropsychological mechanisms of OCD and its pharmacological treatment. Transcriptome-neuroimaging spatial correlation analyses provide insight into the neurobiological mechanisms underlying OCD pathology. Furthermore, SVR analyses hold promise for advancing precision medicine approaches in treating patients with OCD.
Collapse
Affiliation(s)
- Haohao Yan
- Department of Psychiatry, National Clinical Research Center for Mental Disorders, and National Center for Mental Disorders, The Second Xiangya Hospital of Central South University, Changsha, 410011, Hunan, China
| | - Xiaoxiao Shan
- Department of Psychiatry, National Clinical Research Center for Mental Disorders, and National Center for Mental Disorders, The Second Xiangya Hospital of Central South University, Changsha, 410011, Hunan, China
| | - Huabing Li
- Department of Radiology, The Second Xiangya Hospital of Central South University, Changsha, Hunan, 410011, China
| | - Feng Liu
- Department of Radiology, Tianjin Medical University General Hospital, Tianjin, China
| | - Guojun Xie
- Department of Psychiatry, The Third People's Hospital of Foshan, Foshan, 528000, Guangdong, China
| | - Ping Li
- Department of Psychiatry, Qiqihar Medical University, Qiqihar, Heilongjiang, 161006, China
| | - Wenbin Guo
- Department of Psychiatry, National Clinical Research Center for Mental Disorders, and National Center for Mental Disorders, The Second Xiangya Hospital of Central South University, Changsha, 410011, Hunan, China.
| |
Collapse
|
2
|
Vriend C, de Joode NT, Pouwels PJW, Liu F, Otaduy MCG, Pastorello B, Robertson FC, Ipser J, Lee S, Hezel DM, van Meter PE, Batistuzzo MC, Hoexter MQ, Sheshachala K, Narayanaswamy JC, Venkatasubramanian G, Lochner C, Miguel EC, Reddy YCJ, Shavitt RG, Stein DJ, Wall M, Simpson HB, van den Heuvel OA. Age of onset of obsessive-compulsive disorder differentially affects white matter microstructure. Mol Psychiatry 2024; 29:1033-1045. [PMID: 38228890 PMCID: PMC11176057 DOI: 10.1038/s41380-023-02390-8] [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: 10/07/2023] [Revised: 12/04/2023] [Accepted: 12/15/2023] [Indexed: 01/18/2024]
Abstract
Previous diffusion MRI studies have reported mixed findings on white matter microstructure alterations in obsessive-compulsive disorder (OCD), likely due to variation in demographic and clinical characteristics, scanning methods, and underpowered samples. The OCD global study was created across five international sites to overcome these challenges by harmonizing data collection to identify consistent brain signatures of OCD that are reproducible and generalizable. Single-shell diffusion measures (e.g., fractional anisotropy), multi-shell Neurite Orientation Dispersion and Density Imaging (NODDI) and fixel-based measures, were extracted from skeletonized white matter tracts in 260 medication-free adults with OCD and 252 healthy controls. We additionally performed structural connectome analysis. We compared cases with controls and cases with early (<18) versus late (18+) OCD onset using mixed-model and Bayesian multilevel analysis. Compared with healthy controls, adult OCD individuals showed higher fiber density in the sagittal stratum (B[SE] = 0.10[0.05], P = 0.04) and credible evidence for higher fiber density in several other tracts. When comparing early (n = 145) and late-onset (n = 114) cases, converging evidence showed lower integrity of the posterior thalamic radiation -particularly radial diffusivity (B[SE] = 0.28[0.12], P = 0.03)-and lower global efficiency of the structural connectome (B[SE] = 15.3[6.6], P = 0.03) in late-onset cases. Post-hoc analyses indicated divergent direction of effects of the two OCD groups compared to healthy controls. Age of OCD onset differentially affects the integrity of thalamo-parietal/occipital tracts and the efficiency of the structural brain network. These results lend further support for the role of the thalamus and its afferent fibers and visual attentional processes in the pathophysiology of OCD.
Collapse
Affiliation(s)
- Chris Vriend
- Amsterdam UMC, Vrije Universiteit Amsterdam, Department of Psychiatry, and Department of Anatomy and Neuroscience, de Boelelaan 1117, Amsterdam, the Netherlands.
- Compulsivity, Impulsivity and Attention, Amsterdam Neuroscience, de Boelelaan 1117, Amsterdam, the Netherlands.
| | - Niels T de Joode
- Amsterdam UMC, Vrije Universiteit Amsterdam, Department of Psychiatry, and Department of Anatomy and Neuroscience, de Boelelaan 1117, Amsterdam, the Netherlands
- Compulsivity, Impulsivity and Attention, Amsterdam Neuroscience, de Boelelaan 1117, Amsterdam, the Netherlands
| | - Petra J W Pouwels
- Amsterdam UMC, Vrije Universiteit Amsterdam, Department of Radiology and Nuclear Medicine, Amsterdam Neuroscience, de Boelelaan 1117, Amsterdam, the Netherlands
- Brain Imaging, Amsterdam Neuroscience, de Boelelaan 1117, Amsterdam, the Netherlands
| | - Feng Liu
- Columbia University Irving Medical Center, Columbia University, New York, NY, 10032, USA
- The New York State Psychiatric Institute, New York, NY, 10032, USA
| | - Maria C G Otaduy
- LIM44, Hospital das Clinicas HCFMUSP, Instituto e Departamento de Radiologia da Faculdade de Medicina, Universidade de São Paulo, São Paulo, SP, Brazil
| | - Bruno Pastorello
- LIM44, Hospital das Clinicas HCFMUSP, Instituto e Departamento de Radiologia da Faculdade de Medicina, Universidade de São Paulo, São Paulo, SP, Brazil
| | - Frances C Robertson
- Cape Universities Body Imaging Centre, University of Cape Town, Cape Town, South Africa
| | - Jonathan Ipser
- SAMRC Unit on Risk & Resilience in Mental Disorders, Department of Psychiatry & Neuroscience Institute, University of Cape Town, Cape Town, South Africa
| | - Seonjoo Lee
- Columbia University Irving Medical Center, Columbia University, New York, NY, 10032, USA
- The New York State Psychiatric Institute, New York, NY, 10032, USA
| | - Dianne M Hezel
- Columbia University Irving Medical Center, Columbia University, New York, NY, 10032, USA
- The New York State Psychiatric Institute, New York, NY, 10032, USA
| | - Page E van Meter
- Columbia University Irving Medical Center, Columbia University, New York, NY, 10032, USA
- The New York State Psychiatric Institute, New York, NY, 10032, USA
| | - Marcelo C Batistuzzo
- Obsessive-Compulsive Spectrum Disorders Program, LIM23, Hospital das Clinicas HCFMUSP, Instituto & Departamento de Psiquiatria da Faculdade de Medicina, Universidade de São Paulo, São Paulo, SP, Brazil
- Department of Methods and Techniques in Psychology, Pontifical Catholic University, Sao Paulo, SP, Brazil
| | - Marcelo Q Hoexter
- LIM44, Hospital das Clinicas HCFMUSP, Instituto e Departamento de Radiologia da Faculdade de Medicina, Universidade de São Paulo, São Paulo, SP, Brazil
| | - Karthik Sheshachala
- National Institute of Mental Health & Neurosciences (NIMHANS), Bangalore, India
| | | | | | - Christine Lochner
- SAMRC Unit on Risk & Resilience in Mental Disorders, Department of Psychiatry, Stellenbosch University, Stellenbosch, South Africa
| | - Euripedes C Miguel
- Obsessive-Compulsive Spectrum Disorders Program, LIM23, Hospital das Clinicas HCFMUSP, Instituto & Departamento de Psiquiatria da Faculdade de Medicina, Universidade de São Paulo, São Paulo, SP, Brazil
| | - Y C Janardhan Reddy
- National Institute of Mental Health & Neurosciences (NIMHANS), Bangalore, India
| | - Roseli G Shavitt
- Obsessive-Compulsive Spectrum Disorders Program, LIM23, Hospital das Clinicas HCFMUSP, Instituto & Departamento de Psiquiatria da Faculdade de Medicina, Universidade de São Paulo, São Paulo, SP, Brazil
| | - Dan J Stein
- SAMRC Unit on Risk & Resilience in Mental Disorders, Department of Psychiatry, Stellenbosch University, Stellenbosch, South Africa
| | - Melanie Wall
- Columbia University Irving Medical Center, Columbia University, New York, NY, 10032, USA
- The New York State Psychiatric Institute, New York, NY, 10032, USA
| | - Helen Blair Simpson
- Columbia University Irving Medical Center, Columbia University, New York, NY, 10032, USA
- The New York State Psychiatric Institute, New York, NY, 10032, USA
| | - Odile A van den Heuvel
- Amsterdam UMC, Vrije Universiteit Amsterdam, Department of Psychiatry, and Department of Anatomy and Neuroscience, de Boelelaan 1117, Amsterdam, the Netherlands
- Compulsivity, Impulsivity and Attention, Amsterdam Neuroscience, de Boelelaan 1117, Amsterdam, the Netherlands
| |
Collapse
|
3
|
Shobeiri P, Hosseini Shabanan S, Haghshomar M, Khanmohammadi S, Fazeli S, Sotoudeh H, Kamali A. Cerebellar Microstructural Abnormalities in Obsessive-Compulsive Disorder (OCD): a Systematic Review of Diffusion Tensor Imaging Studies. CEREBELLUM (LONDON, ENGLAND) 2024; 23:778-801. [PMID: 37291229 DOI: 10.1007/s12311-023-01573-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Accepted: 05/22/2023] [Indexed: 06/10/2023]
Abstract
Previous neuroimaging studies have suggested that obsessive-compulsive disorder (OCD) is associated with altered resting-state functional connectivity of the cerebellum. In this study, we aimed to describe the most significant and reproducible microstructural abnormalities and cerebellar changes associated with obsessive-compulsive disorder (OCD) using diffusion tensor imaging (DTI) investigations. PubMed and EMBASE were searched for relevant studies using the PRISMA 2020 protocol. A total of 17 publications were chosen for data synthesis after screening titles and abstracts, full-text examination, and executing the inclusion criteria. The patterns of cerebellar white matter (WM) integrity loss, determined by fractional anisotropy (FA), mean diffusivity (MD), radial diffusivity (RD), and axial diffusivity (AD) metrics, varied across studies and symptoms. Changes in fractional anisotropy (FA) values were described in six publications, which were decreased in four and increased in two studies. An increase in diffusivity parameters of the cerebellum (i.e., MD, RD, and AD) in OCD patients was reported in four studies. Alterations of the cerebellar connectivity with other brain areas were also detected in three studies. Heterogenous results were found in studies that investigated cerebellar microstructural abnormalities in correlation with symptom dimension or severity. OCD's complex phenomenology may be characterized by changes in cerebellar WM connectivity across wide networks, as shown by DTI studies on OCD patients in both children and adults. Classification features in machine learning and clinical tools for diagnosing OCD and determining the prognosis of the disorder might both benefit from using cerebellar DTI data.
Collapse
Affiliation(s)
- Parnian Shobeiri
- Faculty of Medicine, Tehran University of Medical Sciences, Tehran, Iran.
- NeuroImaging Network (NIN), Universal Scientific Education and Research Network (USERN), Tehran, Iran.
- Non-Communicable Diseases Research Center, Endocrinology and Metabolism Population Sciences Institute, Tehran University of Medical Sciences, Tehran, Iran.
| | | | - Maryam Haghshomar
- NeuroImaging Network (NIN), Universal Scientific Education and Research Network (USERN), Tehran, Iran
- Department of Radiology, Northwestern University, Chicago, IL, USA
| | - Shaghayegh Khanmohammadi
- Faculty of Medicine, Tehran University of Medical Sciences, Tehran, Iran
- Non-Communicable Diseases Research Center, Endocrinology and Metabolism Population Sciences Institute, Tehran University of Medical Sciences, Tehran, Iran
| | - Soudabeh Fazeli
- Department of Radiology, University of California San Diego, San Diego, CA, USA
| | - Houman Sotoudeh
- Department of Radiology and Neurology, University of Alabama at Birmingham (UAB), Birmingham, AL, USA
| | - Arash Kamali
- Department of Diagnostic and Interventional Radiology, University of Texas McGovern Medical School, Houston, TX, USA
| |
Collapse
|
4
|
Xu C, Hou G, He T, Ruan Z, Guo X, Chen J, Wei Z, Seger CA, Chen Q, Peng Z. Local structural and functional MRI markers of compulsive behaviors and obsessive-compulsive disorder diagnosis within striatum-based circuits. Psychol Med 2024; 54:710-720. [PMID: 37642202 DOI: 10.1017/s0033291723002386] [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] [Indexed: 08/31/2023]
Abstract
BACKGROUND Obsessive-compulsive disorder (OCD) is a classic disorder on the compulsivity spectrum, with diverse comorbidities. In the current study, we sought to understand OCD from a dimensional perspective by identifying multimodal neuroimaging patterns correlated with multiple phenotypic characteristics within the striatum-based circuits known to be affected by OCD. METHODS Neuroimaging measurements of local functional and structural features and clinical information were collected from 110 subjects, including 51 patients with OCD and 59 healthy control subjects. Linked independent component analysis (LICA) and correlation analysis were applied to identify associations between local neuroimaging patterns across modalities (including gray matter volume, white matter integrity, and spontaneous functional activity) and clinical factors. RESULTS LICA identified eight multimodal neuroimaging patterns related to phenotypic variations, including three related to symptoms and diagnosis. One imaging pattern (IC9) that included both the amplitude of low-frequency fluctuation measure of spontaneous functional activity and white matter integrity measures correlated negatively with OCD diagnosis and diagnostic scales. Two imaging patterns (IC10 and IC27) correlated with compulsion symptoms: IC10 included primarily anatomical measures and IC27 included primarily functional measures. In addition, we identified imaging patterns associated with age, gender, and emotional expression across subjects. CONCLUSIONS We established that data fusion techniques can identify local multimodal neuroimaging patterns associated with OCD phenotypes. The results inform our understanding of the neurobiological underpinnings of compulsive behaviors and OCD diagnosis.
Collapse
Affiliation(s)
- Chuanyong Xu
- Department of Child Psychiatry and Rehabilitation, Institute of Maternity and Child Medical Research, Affiliated Shenzhen Maternity & Child Healthcare Hospital, Southern Medical University, Shenzhen, China
| | - Gangqiang Hou
- Department of Radiology, Shenzhen Kangning Hospital, Shenzhen, China
| | - Tingxin He
- Key Laboratory of Brain, Cognition and Education Sciences, Ministry of Education, Guangzhou, China
- School of Psychology, Center for Studies of Psychological Application, and Guangdong Key Laboratory of Mental Health and Cognitive Science, South China Normal University, Guangzhou, China
| | - Zhongqiang Ruan
- Key Laboratory of Brain, Cognition and Education Sciences, Ministry of Education, Guangzhou, China
- School of Psychology, Center for Studies of Psychological Application, and Guangdong Key Laboratory of Mental Health and Cognitive Science, South China Normal University, Guangzhou, China
| | - Xinrong Guo
- Department of Child Psychiatry and Rehabilitation, Institute of Maternity and Child Medical Research, Affiliated Shenzhen Maternity & Child Healthcare Hospital, Southern Medical University, Shenzhen, China
| | - Jierong Chen
- Department of Child Psychiatry and Rehabilitation, Institute of Maternity and Child Medical Research, Affiliated Shenzhen Maternity & Child Healthcare Hospital, Southern Medical University, Shenzhen, China
| | - Zhen Wei
- Department of Child Psychiatry and Rehabilitation, Institute of Maternity and Child Medical Research, Affiliated Shenzhen Maternity & Child Healthcare Hospital, Southern Medical University, Shenzhen, China
| | - Carol A Seger
- Key Laboratory of Brain, Cognition and Education Sciences, Ministry of Education, Guangzhou, China
- School of Psychology, Center for Studies of Psychological Application, and Guangdong Key Laboratory of Mental Health and Cognitive Science, South China Normal University, Guangzhou, China
- Department of Psychology, Colorado State University, Fort Collins, Colorado, USA
| | - Qi Chen
- School of Psychology, Shenzhen University, Shenzhen, China
| | - Ziwen Peng
- Key Laboratory of Brain, Cognition and Education Sciences, Ministry of Education, Guangzhou, China
- School of Psychology, Center for Studies of Psychological Application, and Guangdong Key Laboratory of Mental Health and Cognitive Science, South China Normal University, Guangzhou, China
| |
Collapse
|
5
|
Li X, Kang Q, Gu H. A comprehensive review for machine learning on neuroimaging in obsessive-compulsive disorder. Front Hum Neurosci 2023; 17:1280512. [PMID: 38021236 PMCID: PMC10646310 DOI: 10.3389/fnhum.2023.1280512] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/20/2023] [Accepted: 10/18/2023] [Indexed: 12/01/2023] Open
Abstract
Obsessive-compulsive disorder (OCD) is a common mental disease, which can exist as a separate disease or become one of the symptoms of other mental diseases. With the development of society, statistically, the incidence rate of obsessive-compulsive disorder has been increasing year by year. At present, in the diagnosis and treatment of OCD, The clinical performance of patients measured by scales is no longer the only quantitative indicator. Clinical workers and researchers are committed to using neuroimaging to explore the relationship between changes in patient neurological function and obsessive-compulsive disorder. Through machine learning and artificial learning, medical information in neuroimaging can be better displayed. In this article, we discuss recent advancements in artificial intelligence related to neuroimaging in the context of Obsessive-Compulsive Disorder.
Collapse
Affiliation(s)
- Xuanyi Li
- Department of Radiology, Shengjing Hospital of China Medical University, Shenyang, Liaoning, China
| | - Qiang Kang
- Department of Radiology, Xing’an League People’s Hospital of Inner Mongolia, Mongolia, China
| | - Hanxing Gu
- Department of Geriatric Psychiatry, Qingdao Mental Health Center, Qingdao, Shandong, China
| |
Collapse
|
6
|
Lv Q, Zeljic K, Zhao S, Zhang J, Zhang J, Wang Z. Dissecting Psychiatric Heterogeneity and Comorbidity with Core Region-Based Machine Learning. Neurosci Bull 2023; 39:1309-1326. [PMID: 37093448 PMCID: PMC10387015 DOI: 10.1007/s12264-023-01057-2] [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: 09/02/2022] [Accepted: 02/17/2023] [Indexed: 04/25/2023] Open
Abstract
Machine learning approaches are increasingly being applied to neuroimaging data from patients with psychiatric disorders to extract brain-based features for diagnosis and prognosis. The goal of this review is to discuss recent practices for evaluating machine learning applications to obsessive-compulsive and related disorders and to advance a novel strategy of building machine learning models based on a set of core brain regions for better performance, interpretability, and generalizability. Specifically, we argue that a core set of co-altered brain regions (namely 'core regions') comprising areas central to the underlying psychopathology enables the efficient construction of a predictive model to identify distinct symptom dimensions/clusters in individual patients. Hypothesis-driven and data-driven approaches are further introduced showing how core regions are identified from the entire brain. We demonstrate a broadly applicable roadmap for leveraging this core set-based strategy to accelerate the pursuit of neuroimaging-based markers for diagnosis and prognosis in a variety of psychiatric disorders.
Collapse
Affiliation(s)
- Qian Lv
- School of Psychological and Cognitive Sciences, Beijing Key Laboratory of Behavior and Mental Health, IDG/McGovern Institute for Brain Research, Peking-Tsinghua Center for Life Sciences, Peking University, Beijing, 100871, China.
| | - Kristina Zeljic
- School of Health and Psychological Sciences, City, University of London, London, EC1V 0HB, UK
| | - Shaoling Zhao
- Institute of Neuroscience, State Key Laboratory of Neuroscience, CAS Center for Excellence in Brain Science and Intelligence Technology, Chinese Academy of Sciences, Shanghai, 200031, China
- University of Chinese Academy of Sciences, Beijing, 101408, China
| | - Jiangtao Zhang
- Tongde Hospital of Zhejiang Province (Zhejiang Mental Health Center), Zhejiang Office of Mental Health, Hangzhou, 310012, China
| | - Jianmin Zhang
- Tongde Hospital of Zhejiang Province (Zhejiang Mental Health Center), Zhejiang Office of Mental Health, Hangzhou, 310012, China
| | - Zheng Wang
- School of Psychological and Cognitive Sciences, Beijing Key Laboratory of Behavior and Mental Health, IDG/McGovern Institute for Brain Research, Peking-Tsinghua Center for Life Sciences, Peking University, Beijing, 100871, China.
- School of Biomedical Engineering, Hainan University, Haikou, 570228, China.
| |
Collapse
|
7
|
Chen Z, Hu B, Liu X, Becker B, Eickhoff SB, Miao K, Gu X, Tang Y, Dai X, Li C, Leonov A, Xiao Z, Feng Z, Chen J, Chuan-Peng H. Sampling inequalities affect generalization of neuroimaging-based diagnostic classifiers in psychiatry. BMC Med 2023; 21:241. [PMID: 37400814 DOI: 10.1186/s12916-023-02941-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/10/2023] [Accepted: 06/13/2023] [Indexed: 07/05/2023] Open
Abstract
BACKGROUND The development of machine learning models for aiding in the diagnosis of mental disorder is recognized as a significant breakthrough in the field of psychiatry. However, clinical practice of such models remains a challenge, with poor generalizability being a major limitation. METHODS Here, we conducted a pre-registered meta-research assessment on neuroimaging-based models in the psychiatric literature, quantitatively examining global and regional sampling issues over recent decades, from a view that has been relatively underexplored. A total of 476 studies (n = 118,137) were included in the current assessment. Based on these findings, we built a comprehensive 5-star rating system to quantitatively evaluate the quality of existing machine learning models for psychiatric diagnoses. RESULTS A global sampling inequality in these models was revealed quantitatively (sampling Gini coefficient (G) = 0.81, p < .01), varying across different countries (regions) (e.g., China, G = 0.47; the USA, G = 0.58; Germany, G = 0.78; the UK, G = 0.87). Furthermore, the severity of this sampling inequality was significantly predicted by national economic levels (β = - 2.75, p < .001, R2adj = 0.40; r = - .84, 95% CI: - .41 to - .97), and was plausibly predictable for model performance, with higher sampling inequality for reporting higher classification accuracy. Further analyses showed that lack of independent testing (84.24% of models, 95% CI: 81.0-87.5%), improper cross-validation (51.68% of models, 95% CI: 47.2-56.2%), and poor technical transparency (87.8% of models, 95% CI: 84.9-90.8%)/availability (80.88% of models, 95% CI: 77.3-84.4%) are prevailing in current diagnostic classifiers despite improvements over time. Relating to these observations, model performances were found decreased in studies with independent cross-country sampling validations (all p < .001, BF10 > 15). In light of this, we proposed a purpose-built quantitative assessment checklist, which demonstrated that the overall ratings of these models increased by publication year but were negatively associated with model performance. CONCLUSIONS Together, improving sampling economic equality and hence the quality of machine learning models may be a crucial facet to plausibly translating neuroimaging-based diagnostic classifiers into clinical practice.
Collapse
Affiliation(s)
- Zhiyi Chen
- Experimental Research Center for Medical and Psychological Science (ERC-MPS), School of Psychology, Third Military Medical University, Chongqing, China.
- Faculty of Psychology, Southwest University, Chongqing, China.
| | - Bowen Hu
- Faculty of Psychology, Southwest University, Chongqing, China
| | - Xuerong Liu
- Experimental Research Center for Medical and Psychological Science (ERC-MPS), School of Psychology, Third Military Medical University, Chongqing, China
| | - Benjamin Becker
- The Center of Psychosomatic Medicine, Sichuan Provincial Center for Mental Health, Sichuan Provincial People's Hospital, Chengdu, China
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Laboratory for Neuroinformation, University of Electronic Science and Technology of China, Chengdu, China
| | - Simon B Eickhoff
- Institute of Systems Neuroscience, Heinrich Heine University Düsseldorf, Düsseldorf, Germany
| | - Kuan Miao
- Experimental Research Center for Medical and Psychological Science (ERC-MPS), School of Psychology, Third Military Medical University, Chongqing, China
| | - Xingmei Gu
- Experimental Research Center for Medical and Psychological Science (ERC-MPS), School of Psychology, Third Military Medical University, Chongqing, China
| | - Yancheng Tang
- School of Business and Management, Shanghai International Studies University, Shanghai, China
| | - Xin Dai
- Faculty of Psychology, Southwest University, Chongqing, China
| | - Chao Li
- Department of Radiology, The Third Affiliated Hospital, Sun Yat-Sen University, Guangdong, China
| | - Artemiy Leonov
- School of Psychology, Clark University, Worcester, MA, USA
| | - Zhibing Xiao
- State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing, China
| | - Zhengzhi Feng
- Experimental Research Center for Medical and Psychological Science (ERC-MPS), School of Psychology, Third Military Medical University, Chongqing, China
| | - Ji Chen
- Department of Psychology and Behavioral Sciences, Zhejiang University, Hangzhou, China.
- Department of Psychiatry, The Fourth Affiliated Hospital, Zhejiang University School of Medicine, Yiwu, Zhejiang, China.
| | - Hu Chuan-Peng
- School of Psychology, Nanjing Normal University, Nanjing, China
| |
Collapse
|
8
|
Wang H, Yao R, Zhang X, Chen C, Wu J, Dong M, Jin C. Visual expertise modulates resting-state brain network dynamics in radiologists: a degree centrality analysis. Front Neurosci 2023; 17:1152619. [PMID: 37266545 PMCID: PMC10229894 DOI: 10.3389/fnins.2023.1152619] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/28/2023] [Accepted: 04/26/2023] [Indexed: 06/03/2023] Open
Abstract
Visual expertise reflects accumulated experience in reviewing domain-specific images and has been shown to modulate brain function in task-specific functional magnetic resonance imaging studies. However, little is known about how visual experience modulates resting-state brain network dynamics. To explore this, we recruited 22 radiology interns and 22 matched healthy controls and used resting-state functional magnetic resonance imaging (rs-fMRI) and the degree centrality (DC) method to investigate changes in brain network dynamics. Our results revealed significant differences in DC between the RI and control group in brain regions associated with visual processing, decision making, memory, attention control, and working memory. Using a recursive feature elimination-support vector machine algorithm, we achieved a classification accuracy of 88.64%. Our findings suggest that visual experience modulates resting-state brain network dynamics in radiologists and provide new insights into the neural mechanisms of visual expertise.
Collapse
Affiliation(s)
- Hongmei Wang
- Department of Radiology, First Affiliated Hospital of Xi'an, Jiaotong University, Xi'an, China
- Department of Medical Imaging, Inner Mongolia People's Hospital, Hohhot, China
| | - Renhuan Yao
- Department of Nuclear Medicine, Inner Mongolia People's Hospital, Hohhot, China
| | - Xiaoyan Zhang
- Engineering Research Center of Molecular and Neuro Imaging of Ministry of Education, School of Life Science and Technology, Xidian University, Xi'an, China
| | - Chao Chen
- PLA Funding Payment Center, Beijing, China
| | - Jia Wu
- School of Foreign Languages, Northwestern Polytechnical University, Xi'an, Shaanxi, China
| | - Minghao Dong
- Engineering Research Center of Molecular and Neuro Imaging of Ministry of Education, School of Life Science and Technology, Xidian University, Xi'an, China
- Xi'an Key Laboratory of Intelligent Sensing and Regulation of Trans-Scale Life Information, School of Life Science and Technology, Xidian University, Xi'an, China
| | - Chenwang Jin
- Department of Radiology, First Affiliated Hospital of Xi'an, Jiaotong University, Xi'an, China
| |
Collapse
|
9
|
Zühlsdorff K, Dalley JW, Robbins TW, Morein-Zamir S. Cognitive flexibility: neurobehavioral correlates of changing one's mind. Cereb Cortex 2023; 33:5436-5446. [PMID: 36368894 PMCID: PMC10152092 DOI: 10.1093/cercor/bhac431] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/13/2022] [Revised: 10/06/2022] [Accepted: 10/07/2022] [Indexed: 11/13/2022] Open
Abstract
Behavioral and cognitive flexibility allow adaptation to a changing environment. Most tasks used to investigate flexibility require switching reactively in response to deterministic task-response rules. In daily life, flexibility often involves a volitional decision to change behavior. This can be instigated by environmental signals, but these are frequently unreliable. We report results from a novel "change your mind" task, which assesses volitional switching under uncertainty without the need for rule-based learning. Participants completed a two-alternative choice task, and following spurious feedback, were presented with the same stimulus again. Subjects had the opportunity to repeat or change their response. Forty healthy participants completed the task while undergoing a functional magnetic resonance imaging scan. Participants predominantly repeated their choice but changed more when their first response was incorrect or when the feedback was negative. Greater activations for changing were found in the inferior frontal junction, anterior insula (AI), anterior cingulate, and dorsolateral prefrontal cortex. Changing responses were also accompanied by reduced connectivity from the AI and orbitofrontal cortices to the occipital cortex. Using multivariate pattern analysis of brain activity, we predicted with 77% reliability whether participants would change their mind. These findings extend our understanding of cognitive flexibility in daily life by assessing volitional decision-making.
Collapse
Affiliation(s)
- Katharina Zühlsdorff
- Department of Psychology, University of Cambridge, Downing Place, Cambridge, CB2 3EB, United Kingdom
- The Alan Turing Institute, British Library, 96 Euston Road, London, NW1 2DB, United Kingdom
- Behavioural and Clinical Neuroscience Institute, Department of Psychology, University of Cambridge, Downing Street, Cambridge, CB2 3EB, United Kingdom
| | - Jeffrey W Dalley
- Department of Psychology, University of Cambridge, Downing Place, Cambridge, CB2 3EB, United Kingdom
- Behavioural and Clinical Neuroscience Institute, Department of Psychology, University of Cambridge, Downing Street, Cambridge, CB2 3EB, United Kingdom
- Department of Psychiatry, University of Cambridge, Herchel Smith Building, Forvie Site, Robinson Way, Cambridge, CB2 0SZ, United Kingdom
| | - Trevor W Robbins
- Department of Psychology, University of Cambridge, Downing Place, Cambridge, CB2 3EB, United Kingdom
- Behavioural and Clinical Neuroscience Institute, Department of Psychology, University of Cambridge, Downing Street, Cambridge, CB2 3EB, United Kingdom
| | - Sharon Morein-Zamir
- School of Psychology and Sport Science, Anglia Ruskin University, East Road, Cambridge, CB1 1PT, United Kingdom
| |
Collapse
|
10
|
Shi D, Ren Z, Zhang H, Wang G, Guo Q, Wang S, Ding J, Yao X, Li Y, Ren K. Amplitude of low-frequency fluctuation-based regional radiomics similarity network: Biomarker for Parkinson's disease. Heliyon 2023; 9:e14325. [PMID: 36950566 PMCID: PMC10025115 DOI: 10.1016/j.heliyon.2023.e14325] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/19/2022] [Revised: 01/18/2023] [Accepted: 02/28/2023] [Indexed: 03/08/2023] Open
Abstract
Parkinson's disease (PD) is a highly heterogeneous disorder that is difficult to diagnose. Therefore, reliable biomarkers are needed. We implemented a method constructing a regional radiomics similarity network (R2SN) based on the amplitude of low-frequency fluctuation (ALFF). We classified patients with PD and healthy individuals by using a machine learning approach in accordance with the R2SN connectome. The ALFF-based R2SN exhibited great reproducibility with different brain atlases and datasets. Great classification performances were achieved both in primary (AUC = 0.85 ± 0.02 and accuracy = 0.81 ± 0.03) and independent external validation (AUC = 0.77 and accuracy = 0.70) datasets. The discriminative R2SN edges correlated with the clinical evaluations of patients with PD. The nodes of discriminative R2SN edges were primarily located in the default mode, sensorimotor, executive control, visual and frontoparietal network, cerebellum and striatum. These findings demonstrate that ALFF-based R2SN is a robust potential neuroimaging biomarker for PD and could provide new insights into connectome reorganization in PD.
Collapse
Affiliation(s)
- Dafa Shi
- Department of Radiology, Xiang’an Hospital of Xiamen University, School of Medicine, Xiamen University, Xiamen, China
| | - Zhendong Ren
- Department of Radiology, Xiang’an Hospital of Xiamen University, School of Medicine, Xiamen University, Xiamen, China
| | - Haoran Zhang
- Department of Radiology, Xiang’an Hospital of Xiamen University, School of Medicine, Xiamen University, Xiamen, China
| | - Guangsong Wang
- Department of Radiology, Xiang’an Hospital of Xiamen University, School of Medicine, Xiamen University, Xiamen, China
| | - Qiu Guo
- Department of Radiology, Xiang’an Hospital of Xiamen University, School of Medicine, Xiamen University, Xiamen, China
| | - Siyuan Wang
- Department of Radiology, Xiang’an Hospital of Xiamen University, School of Medicine, Xiamen University, Xiamen, China
| | - Jie Ding
- Department of Radiology, Xiang’an Hospital of Xiamen University, School of Medicine, Xiamen University, Xiamen, China
| | - Xiang Yao
- Department of Radiology, Xiang’an Hospital of Xiamen University, School of Medicine, Xiamen University, Xiamen, China
| | - Yanfei Li
- Department of Radiology, Xiang’an Hospital of Xiamen University, School of Medicine, Xiamen University, Xiamen, China
| | - Ke Ren
- Department of Radiology, Xiang’an Hospital of Xiamen University, School of Medicine, Xiamen University, Xiamen, China
- Xiamen Key Laboratory for Endocrine-Related Cancer Precision Medicine, Xiang’an Hospital of Xiamen University, School of Medicine, Xiamen University, Xiamen, China
- Corresponding author. Department of Radiology, Xiang’an Hospital of Xiamen University, School of Medicine, Xiamen University, Xiamen, China.
| |
Collapse
|
11
|
Li Y, Yu Z, Zhou X, Wu P, Chen J. Aberrant interhemispheric functional reciprocities of the default mode network and motor network in subcortical ischemic stroke patients with motor impairment: A longitudinal study. Front Neurol 2022; 13:996621. [PMID: 36267883 PMCID: PMC9577250 DOI: 10.3389/fneur.2022.996621] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/18/2022] [Accepted: 09/15/2022] [Indexed: 11/23/2022] Open
Abstract
Purpose The purpose of the present study was to explore the longitudinal changes in functional homotopy in the default mode network (DMN) and motor network and its relationships with clinical characteristics in patients with stroke. Methods Resting-state functional magnetic resonance imaging was performed in stroke patients with subcortical ischemic lesions and healthy controls. The voxel-mirrored homotopic connectivity (VMHC) method was used to examine the differences in functional homotopy in patients with stroke between the two time points. Support vector machine (SVM) and correlation analyses were also applied to investigate whether the detected significant changes in VMHC were the specific feature in patients with stroke. Results The patients with stroke had significantly lower VMHC in the DMN and motor-related regions than the controls, including in the precuneus, parahippocampus, precentral gyrus, supplementary motor area, and middle frontal gyrus. Longitudinal analysis revealed that the impaired VMHC of the superior precuneus showed a significant increase at the second time point, which was no longer significantly different from the controls. Between the two time points, the changes in VMHC in the superior precuneus were significantly correlated with the changes in clinical scores. SVM analysis revealed that the VMHC of the superior precuneus could be used to correctly identify the patients with stroke from the controls with a statistically significant accuracy of 81.25% (P ≤ 0.003). Conclusions Our findings indicated that the increased VMHC in the superior precuneus could be regarded as the neuroimaging manifestation of functional recovery. The significant correlation and the discriminative power in classification results might provide novel evidence to understand the neural mechanisms responsible for brain reorganization after stroke.
Collapse
Affiliation(s)
- Yongxin Li
- School of Traditional Chinese Medicine, Formula-Pattern Research Center, Jinan University, Guangzhou, China
- *Correspondence: Yongxin Li
| | - Zeyun Yu
- Acupuncture and Tuina School/Tird Teaching Hospital, Chengdu University of Traditional Chinese Medicine, Chengdu, China
| | - Xuan Zhou
- School of Traditional Chinese Medicine, Formula-Pattern Research Center, Jinan University, Guangzhou, China
| | - Ping Wu
- Acupuncture and Tuina School/Tird Teaching Hospital, Chengdu University of Traditional Chinese Medicine, Chengdu, China
- Ping Wu
| | - Jiaxu Chen
- School of Traditional Chinese Medicine, Formula-Pattern Research Center, Jinan University, Guangzhou, China
| |
Collapse
|
12
|
Fu C, Zhang Y, Ye Y, Hou X, Wen Z, Yan Z, Luo W, Feng M, Liu B. Predicting response to tVNS in patients with migraine using functional MRI: A voxels-based machine learning analysis. Front Neurosci 2022; 16:937453. [PMID: 35992927 PMCID: PMC9388938 DOI: 10.3389/fnins.2022.937453] [Citation(s) in RCA: 9] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/06/2022] [Accepted: 07/13/2022] [Indexed: 11/13/2022] Open
Abstract
BackgroundMigraine is a common disorder, affecting many patients. However, for one thing, lacking objective biomarkers, misdiagnosis, and missed diagnosis happen occasionally. For another, though transcutaneous vagus nerve stimulation (tVNS) could alleviate migraine symptoms, the individual difference of tVNS efficacy in migraineurs hamper the clinical application of tVNS. Therefore, it is necessary to identify biomarkers to discriminate migraineurs as well as select patients suitable for tVNS treatment.MethodsA total of 70 patients diagnosed with migraine without aura (MWoA) and 70 matched healthy controls were recruited to complete fMRI scanning. In study 1, the fractional amplitude of low-frequency fluctuation (fALFF) of each voxel was calculated, and the differences between healthy controls and MWoA were compared. Meaningful voxels were extracted as features for discriminating model construction by a support vector machine. The performance of the discriminating model was assessed by accuracy, sensitivity, and specificity. In addition, a mask of these significant brain regions was generated for further analysis. Then, in study 2, 33 of the 70 patients with MWoA in study 1 receiving real tVNS were included to construct the predicting model in the generated mask. Discriminative features of the discriminating model in study 1 were used to predict the reduction of attack frequency after a 4-week tVNS treatment by support vector regression. A correlation coefficient between predicted value and actual value of the reduction of migraine attack frequency was conducted in 33 patients to assess the performance of predicting model after tVNS treatment. We vislized the distribution of the predictive voxels as well as investigated the association between fALFF change (post-per treatment) of predict weight brain regions and clinical outcomes (frequency of migraine attack) in the real group.ResultsA biomarker containing 3,650 features was identified with an accuracy of 79.3%, sensitivity of 78.6%, and specificity of 80.0% (p < 0.002). The discriminative features were found in the trigeminal cervical complex/rostral ventromedial medulla (TCC/RVM), thalamus, medial prefrontal cortex (mPFC), and temporal gyrus. Then, 70 of 3,650 discriminative features were identified to predict the reduction of attack frequency after tVNS treatment with a correlation coefficient of 0.36 (p = 0.03). The 70 predictive features were involved in TCC/RVM, mPFC, temporal gyrus, middle cingulate cortex (MCC), and insula. The reduction of migraine attack frequency had a positive correlation with right TCC/RVM (r = 0.433, p = 0.021), left MCC (r = 0.451, p = 0.016), and bilateral mPFC (r = 0.416, p = 0.028), and negative with left insula (r = −0.473, p = 0.011) and right superior temporal gyrus/middle temporal gyrus (r = −0.684, p < 0.001), respectively.ConclusionsBy machine learning, the study proposed two potential biomarkers that could discriminate patients with MWoA and predict the efficacy of tVNS in reducing migraine attack frequency. The pivotal features were mainly located in the TCC/RVM, thalamus, mPFC, and temporal gyrus.
Collapse
Affiliation(s)
- Chengwei Fu
- Department of Radiology, The Second Affiliated Hospital of Guangzhou University of Chinese Medicine, Guangzhou, China
- The Second Clinical College, Guangzhou University of Chinese Medicine, Guangzhou, China
| | - Yue Zhang
- Department of Radiology, The Second Affiliated Hospital of Guangzhou University of Chinese Medicine, Guangzhou, China
| | - Yongsong Ye
- Department of Radiology, The Second Affiliated Hospital of Guangzhou University of Chinese Medicine, Guangzhou, China
| | - Xiaoyan Hou
- Department of Radiology, The Second Affiliated Hospital of Guangzhou University of Chinese Medicine, Guangzhou, China
| | - Zeying Wen
- Department of Radiology, The Second Affiliated Hospital of Guangzhou University of Chinese Medicine, Guangzhou, China
- The Second Clinical College, Guangzhou University of Chinese Medicine, Guangzhou, China
- Department of Radiology, The First Affiliated Hospital of Henan University of Chinese Medicine, Zhengzhou, China
| | - Zhaoxian Yan
- Department of Radiology, The Second Affiliated Hospital of Guangzhou University of Chinese Medicine, Guangzhou, China
| | - Wenting Luo
- Department of Radiology, The Second Affiliated Hospital of Guangzhou University of Chinese Medicine, Guangzhou, China
- The Second Clinical College, Guangzhou University of Chinese Medicine, Guangzhou, China
| | - Menghan Feng
- Department of Radiology, The Second Affiliated Hospital of Guangzhou University of Chinese Medicine, Guangzhou, China
- The Second Clinical College, Guangzhou University of Chinese Medicine, Guangzhou, China
| | - Bo Liu
- Department of Radiology, The Second Affiliated Hospital of Guangzhou University of Chinese Medicine, Guangzhou, China
- *Correspondence: Bo Liu
| |
Collapse
|
13
|
Kalmady SV, Paul AK, Narayanaswamy JC, Agrawal R, Shivakumar V, Greenshaw AJ, Dursun SM, Greiner R, Venkatasubramanian G, Reddy YCJ. Prediction of Obsessive-Compulsive Disorder: Importance of Neurobiology-Aided Feature Design and Cross-Diagnosis Transfer Learning. BIOLOGICAL PSYCHIATRY. COGNITIVE NEUROSCIENCE AND NEUROIMAGING 2022; 7:735-746. [PMID: 34929344 DOI: 10.1016/j.bpsc.2021.12.003] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/05/2021] [Revised: 11/25/2021] [Accepted: 12/07/2021] [Indexed: 06/14/2023]
Abstract
BACKGROUND Machine learning applications using neuroimaging provide a multidimensional, data-driven approach that captures the level of complexity necessary for objectively aiding diagnosis and prognosis in psychiatry. However, models learned from small training samples often have limited generalizability, which continues to be a problem with automated diagnosis of mental illnesses such as obsessive-compulsive disorder (OCD). Earlier studies have shown that features incorporating prior neurobiological knowledge of brain function and combining brain parcellations from various sources can potentially improve the overall prediction. However, it is unknown whether such knowledge-driven methods can provide a performance that is comparable to state-of-the-art approaches based on neural networks. METHODS In this study, we apply a transparent and explainable multiparcellation ensemble learning framework EMPaSchiz (Ensemble algorithm with Multiple Parcellations for Schizophrenia prediction) to the task of predicting OCD, based on a resting-state functional magnetic resonance imaging dataset of 350 subjects. Furthermore, we apply transfer learning using the features found effective for schizophrenia to OCD to leverage the commonality in brain alterations across these psychiatric diagnoses. RESULTS We show that our knowledge-based approach leads to a prediction performance of 80.3% accuracy for OCD diagnosis that is better than domain-agnostic and automated feature design using neural networks. Furthermore, we show that a selection of reduced feature sets can be transferred from schizophrenia to the OCD prediction model without significant loss in prediction performance. CONCLUSIONS This study presents a machine learning framework for OCD prediction with neurobiology-aided feature design using resting-state functional magnetic resonance imaging that is generalizable and reasonably interpretable.
Collapse
Affiliation(s)
- Sunil Vasu Kalmady
- Alberta Machine Intelligence Institute, Department of Computing Science, University of Alberta, Edmonton, Alberta, Canada; Canadian VIGOUR Centre, University of Alberta, Edmonton, Alberta, Canada.
| | - Animesh Kumar Paul
- Alberta Machine Intelligence Institute, Department of Computing Science, University of Alberta, Edmonton, Alberta, Canada
| | - Janardhanan C Narayanaswamy
- OCD Clinic, Department of Psychiatry, National Institute of Mental Health and Neuro Sciences, Bangalore, India; Translational Psychiatry Laboratory, Neurobiology Research Centre, National Institute of Mental Health and Neuro Sciences, Bangalore, India
| | - Rimjhim Agrawal
- Translational Psychiatry Laboratory, Neurobiology Research Centre, National Institute of Mental Health and Neuro Sciences, Bangalore, India
| | - Venkataram Shivakumar
- OCD Clinic, Department of Psychiatry, National Institute of Mental Health and Neuro Sciences, Bangalore, India; Translational Psychiatry Laboratory, Neurobiology Research Centre, National Institute of Mental Health and Neuro Sciences, Bangalore, India
| | - Andrew J Greenshaw
- Department of Psychiatry, Faculty of Medicine and Dentistry, University of Alberta, Edmonton, Alberta, Canada
| | - Serdar M Dursun
- Department of Psychiatry, Faculty of Medicine and Dentistry, University of Alberta, Edmonton, Alberta, Canada
| | - Russell Greiner
- Alberta Machine Intelligence Institute, Department of Computing Science, University of Alberta, Edmonton, Alberta, Canada; Department of Psychiatry, Faculty of Medicine and Dentistry, University of Alberta, Edmonton, Alberta, Canada
| | - Ganesan Venkatasubramanian
- OCD Clinic, Department of Psychiatry, National Institute of Mental Health and Neuro Sciences, Bangalore, India; Translational Psychiatry Laboratory, Neurobiology Research Centre, National Institute of Mental Health and Neuro Sciences, Bangalore, India.
| | - Y C Janardhan Reddy
- OCD Clinic, Department of Psychiatry, National Institute of Mental Health and Neuro Sciences, Bangalore, India; Translational Psychiatry Laboratory, Neurobiology Research Centre, National Institute of Mental Health and Neuro Sciences, Bangalore, India
| |
Collapse
|
14
|
Li Y, Qin B, Chen Q, Chen J. Impaired Functional Homotopy and Topological Properties Within the Default Mode Network of Children With Generalized Tonic-Clonic Seizures: A Resting-State fMRI Study. Front Neurosci 2022; 16:833837. [PMID: 35720710 PMCID: PMC9201640 DOI: 10.3389/fnins.2022.833837] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/12/2021] [Accepted: 04/27/2022] [Indexed: 12/02/2022] Open
Abstract
Introduction The aim of the present study was to examine interhemispheric functional connectivity (FC) and topological organization within the default-mode network (DMN) in children with generalized tonic-clonic seizures (GTCS). Methods Resting-state functional MRI was collected in 24 children with GTCS and 34 age-matched typically developing children (TDC). Between-group differences in interhemispheric FC were examined by an automated voxel-mirrored homotopic connectivity (VMHC) method. The topological properties within the DMN were also analyzed using graph theoretical approaches. Consistent results were detected and the VMHC values were extracted as features in machine learning for subject classification. Results Children with GTCS showed a significant decrease in VMHC in the DMN, including the hippocampal formation (HF), lateral temporal cortex (LTC), and angular and middle frontal gyrus. Although the patients exhibited efficient small-world properties of the DMN similar to the TDC, significant changes in regional topological organization were found in the patients, involving the areas of the bilateral temporal parietal junction, bilateral LTC, left temporal pole, and HF. Within the DMN, disrupted interhemispheric FC was found between the bilateral HF and LTC, which was consistent with the VMHC results. The VMHC values in bilateral HF and LTC were significantly correlated with clinical information in patients. Support vector machine analysis using average VMHC information in the bilateral HF and LTC as features achieved a correct classification rate of 89.34% for the classification. Conclusion These results indicate that decreased homotopic coordination in the DMN can be used as an effective biomarker to reflect seizure effects and to distinguish children with GTCSs from TDC.
Collapse
Affiliation(s)
- Yongxin Li
- Formula-Pattern Research Center, School of Traditional Chinese Medicine, Jinan University, Guangzhou, China
- *Correspondence: Yongxin Li,
| | - Bing Qin
- Department of Neurosurgery, Epilepsy Center, The First Affiliated Hospital, Jinan University, Guangzhou, China
| | - Qian Chen
- Department of Pediatric Neurosurgery, Shenzhen Children’s Hospital, Shenzhen, China
- Qian Chen,
| | - Jiaxu Chen
- Formula-Pattern Research Center, School of Traditional Chinese Medicine, Jinan University, Guangzhou, China
- Jiaxu Chen,
| |
Collapse
|
15
|
Guo T, Xuan M, Zhou C, Wu J, Gao T, Bai X, Liu X, Gu L, Liu R, Song Z, Gu Q, Huang P, Pu J, Zhang B, Xu X, Guan X, Zhang M. Normalization effect of levodopa on hierarchical brain function in Parkinson’s disease. Netw Neurosci 2022; 6:552-569. [PMID: 35733432 PMCID: PMC9208001 DOI: 10.1162/netn_a_00232] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/05/2021] [Accepted: 01/10/2022] [Indexed: 11/08/2022] Open
Abstract
Hierarchical brain organization, in which the rich club and diverse club situate in core position, is critical for global information integration in the human brain network. Parkinson’s disease (PD), a common movement disorder, has been conceptualized as a network disorder. Levodopa is an effective treatment for PD. Whether there is a functional divergence in the hierarchical brain system under PD pathology, and how this divergence is regulated by immediate levodopa therapy, remains unknown. We constructed a functional network in 61 PD patients and 89 normal controls and applied graph theoretical analyses to examine the neural mechanism of levodopa short response from the perspective of brain hierarchical configuration. The results revealed the following: (a) PD patients exhibited disrupted function within rich-club organization, while the diverse club preserved function, indicating a differentiated brain topological organization in PD. (b) Along the rich-club derivate hierarchical system, PD patients showed impaired network properties within rich-club and feeder subnetworks, and decreased nodal degree centrality in rich-club and feeder nodes, along with increased nodal degree in peripheral nodes, suggesting distinct functional patterns in different types of nodes. And (c) levodopa could normalize the abnormal network architecture of the rich-club system. This study provides evidence for levodopa effects on the hierarchical brain system with divergent functions. Many studies of brain networks have revealed densely connected regions forming the rich club and diverse club, which occupy the central position of the hierarchical brain system. Here, we explore the hierarchical topology in Parkinson’s disease (PD) and investigate the neural effect of levodopa on it. We show that within the core position of the hierarchical system, the function of the diverse club is preserved while the function of the rich club is impaired. Along the rich-club hierarchical system, the function of biologically costly rich-club and feeder subnetworks is disrupted, together with an increased function of peripheral nodes, which could be normalized by levodopa. Our study provides evidence of a disparity pattern between different levels of brain hierarchical systems under PD pathology.
Collapse
Affiliation(s)
- Tao Guo
- Department of Radiology, the Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China
| | - Min Xuan
- Department of Radiology, the Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China
| | - Cheng Zhou
- Department of Radiology, the Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China
| | - Jingjing Wu
- Department of Radiology, the Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China
| | - Ting Gao
- Department of Neurology, the Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China
| | - Xueqin Bai
- Department of Radiology, the Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China
| | - Xiaocao Liu
- Department of Radiology, the Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China
| | - Luyan Gu
- Department of Neurology, the Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China
| | - Ruiqi Liu
- School of Information Science and Technology, ShanghaiTech University, Shanghai, China
| | - Zhe Song
- Department of Neurology, the Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China
| | - Quanquan Gu
- Department of Radiology, the Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China
| | - Peiyu Huang
- Department of Radiology, the Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China
| | - Jiali Pu
- Department of Neurology, the Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China
| | - Baorong Zhang
- Department of Neurology, the Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China
| | - Xiaojun Xu
- Department of Radiology, the Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China
| | - Xiaojun Guan
- Department of Radiology, the Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China
| | - Minming Zhang
- Department of Radiology, the Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China
| |
Collapse
|
16
|
Yin T, Sun R, He Z, Chen Y, Yin S, Liu X, Lu J, Ma P, Zhang T, Huang L, Qu Y, Suo X, Lei D, Gong Q, Liang F, Li S, Zeng F. Subcortical-Cortical Functional Connectivity as a Potential Biomarker for Identifying Patients with Functional Dyspepsia. Cereb Cortex 2021; 32:3347-3358. [PMID: 34891153 DOI: 10.1093/cercor/bhab419] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/22/2021] [Revised: 10/21/2021] [Accepted: 10/22/2021] [Indexed: 02/05/2023] Open
Abstract
The diagnosis of functional dyspepsia (FD) presently relies on the self-reported symptoms. This study aimed to determine the potential of functional brain network features as biomarkers for the identification of FD patients. Firstly, the functional brain Magnetic Resonance Imaging data were collected from 100 FD patients and 100 healthy subjects, and the functional brain network features were extracted by the independent component analysis. Then, a support vector machine classifier was established based on these functional brain network features to discriminate FD patients from healthy subjects. Features that contributed substantially to the classification were finally identified as the classifying features. The results demonstrated that the classifier performed pretty well in discriminating FD patients. Namely, the accuracy of classification was 0.84 ± 0.03 in cross-validation set and 0.80 ± 0.07 in independent test set, respectively. A total of 15 connections between the subcortical nucleus (the thalamus and caudate) and sensorimotor cortex, parahippocampus, orbitofrontal cortex were finally determined as the classifying features. Furthermore, the results of cross-brain atlas validation showed that these classifying features were quite robust in the identification of FD patients. In summary, the current findings suggested the potential of using machine learning method and functional brain network biomarkers to identify FD patients.
Collapse
Affiliation(s)
- Tao Yin
- Acupuncture and Tuina School, The 3rd Teaching Hospital, Acupuncture and Brain Science Research Center, Chengdu University of Traditional Chinese Medicine, Chengdu, Sichuan 610075, China
| | - Ruirui Sun
- Acupuncture and Tuina School, The 3rd Teaching Hospital, Acupuncture and Brain Science Research Center, Chengdu University of Traditional Chinese Medicine, Chengdu, Sichuan 610075, China
| | - Zhaoxuan He
- Acupuncture and Tuina School, The 3rd Teaching Hospital, Acupuncture and Brain Science Research Center, Chengdu University of Traditional Chinese Medicine, Chengdu, Sichuan 610075, China.,Key Laboratory of Sichuan Province for Acupuncture and Chronobiology, Chengdu, Sichuan 610075, China
| | - Yuan Chen
- International Education College, Chengdu University of Traditional Chinese Medicine, Chengdu, Sichuan 610075, China
| | - Shuai Yin
- First Affiliated Hospital, Henan University of Traditional Chinese Medicine, Zhengzhou, Henan 450002, China
| | - Xiaoyan Liu
- Acupuncture and Tuina School, The 3rd Teaching Hospital, Acupuncture and Brain Science Research Center, Chengdu University of Traditional Chinese Medicine, Chengdu, Sichuan 610075, China
| | - Jin Lu
- Acupuncture and Tuina School, The 3rd Teaching Hospital, Acupuncture and Brain Science Research Center, Chengdu University of Traditional Chinese Medicine, Chengdu, Sichuan 610075, China
| | - Peihong Ma
- Acupuncture and Tuina School, The 3rd Teaching Hospital, Acupuncture and Brain Science Research Center, Chengdu University of Traditional Chinese Medicine, Chengdu, Sichuan 610075, China.,School of Acupuncture-Moxibustion and Tuina, Beijing University of Chinese Medicine, Beijing 100029, China
| | - Tingting Zhang
- Acupuncture and Tuina School, The 3rd Teaching Hospital, Acupuncture and Brain Science Research Center, Chengdu University of Traditional Chinese Medicine, Chengdu, Sichuan 610075, China
| | - Liuyang Huang
- Acupuncture and Tuina School, The 3rd Teaching Hospital, Acupuncture and Brain Science Research Center, Chengdu University of Traditional Chinese Medicine, Chengdu, Sichuan 610075, China
| | - Yuzhu Qu
- Acupuncture and Tuina School, The 3rd Teaching Hospital, Acupuncture and Brain Science Research Center, Chengdu University of Traditional Chinese Medicine, Chengdu, Sichuan 610075, China
| | - Xueling Suo
- Departments of Radiology, Huaxi Magnetic Resonance Research Center (HMRRC), West China Hospital of Sichuan University, Chengdu, Sichuan 610041, China
| | - Du Lei
- Departments of Radiology, Huaxi Magnetic Resonance Research Center (HMRRC), West China Hospital of Sichuan University, Chengdu, Sichuan 610041, China
| | - Qiyong Gong
- Departments of Radiology, Huaxi Magnetic Resonance Research Center (HMRRC), West China Hospital of Sichuan University, Chengdu, Sichuan 610041, China
| | - Fanrong Liang
- Acupuncture and Tuina School, The 3rd Teaching Hospital, Acupuncture and Brain Science Research Center, Chengdu University of Traditional Chinese Medicine, Chengdu, Sichuan 610075, China
| | - Shenghong Li
- State Key Laboratory of Southwestern Chinese Medicine Resources, Innovative Institute of Chinese Medicine and Pharmacy, Chengdu University of Traditional Chinese Medicine, Chengdu 611137, China
| | - Fang Zeng
- Acupuncture and Tuina School, The 3rd Teaching Hospital, Acupuncture and Brain Science Research Center, Chengdu University of Traditional Chinese Medicine, Chengdu, Sichuan 610075, China.,Key Laboratory of Sichuan Province for Acupuncture and Chronobiology, Chengdu, Sichuan 610075, China
| |
Collapse
|
17
|
Cao X, Wang Z, Chen X, Liu Y, Wang W, Abdoulaye IA, Ju S, Yang X, Wang Y, Guo Y. White matter degeneration in remote brain areas of stroke patients with motor impairment due to basal ganglia lesions. Hum Brain Mapp 2021; 42:4750-4761. [PMID: 34232552 PMCID: PMC8410521 DOI: 10.1002/hbm.25583] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/22/2020] [Revised: 05/15/2021] [Accepted: 06/25/2021] [Indexed: 12/14/2022] Open
Abstract
Diffusion tensor imaging (DTI) studies have revealed distinct white matter (WM) characteristics of the brain following diseases. Beyond the lesion‐symptom maps, stroke is characterized by extensive structural and functional alterations of brain areas remote to local lesions. Here, we further investigated the structural changes over a global level by using DTI data of 10 ischemic stroke patients showing motor impairment due to basal ganglia lesions and 11 healthy controls. DTI data were processed to obtain fractional anisotropy (FA) maps, and multivariate pattern analysis was used to explore brain regions that play an important role in classification based on FA maps. The WM structural network was constructed by the deterministic fiber‐tracking approach. In comparison with the controls, the stroke patients showed FA reductions in the perilesional basal ganglia, brainstem, and bilateral frontal lobes. Using network‐based statistics, we found a significant reduction in the WM subnetwork in stroke patients. We identified the patterns of WM degeneration affecting brain areas remote to the lesions, revealing the abnormal organization of the structural network in stroke patients, which may be helpful in understanding of the neural mechanisms underlying hemiplegia.
Collapse
Affiliation(s)
- Xuejin Cao
- Department of Neurology, Southeast University Zhongda Hospital, Medical School of Southeast University, Nanjing, China
| | - Zan Wang
- Department of Neurology, Southeast University Zhongda Hospital, Medical School of Southeast University, Nanjing, China
| | - Xiaohui Chen
- Department of Radiology, Zhongda Hospital, Jiangsu Key Laboratory of Molecular and Functional Imaging, Medical School of Southeast University, Nanjing, China
| | - Yanli Liu
- Department of Rehabilitation, Southeast University Zhongda Hospital, Nanjing, China
| | - Wei Wang
- Department of Radiology, Zhongda Hospital, Jiangsu Key Laboratory of Molecular and Functional Imaging, Medical School of Southeast University, Nanjing, China
| | - Idriss Ali Abdoulaye
- Department of Neurology, Southeast University Zhongda Hospital, Medical School of Southeast University, Nanjing, China
| | - Shenghong Ju
- Department of Radiology, Zhongda Hospital, Jiangsu Key Laboratory of Molecular and Functional Imaging, Medical School of Southeast University, Nanjing, China
| | - Xi Yang
- Department of Rehabilitation, Southeast University Zhongda Hospital, Nanjing, China
| | - Yuancheng Wang
- Department of Radiology, Zhongda Hospital, Jiangsu Key Laboratory of Molecular and Functional Imaging, Medical School of Southeast University, Nanjing, China
| | - Yijing Guo
- Department of Neurology, Southeast University Zhongda Hospital, Medical School of Southeast University, Nanjing, China.,Department of Neurology, Lishui People's Hospital, Southeast University Zhongda Hospital Lishui Branch, Nanjing, China
| |
Collapse
|
18
|
Li F, Sun H, Biswal BB, Sweeney JA, Gong Q. Artificial intelligence applications in psychoradiology. PSYCHORADIOLOGY 2021; 1:94-107. [PMID: 37881257 PMCID: PMC10594695 DOI: 10.1093/psyrad/kkab009] [Citation(s) in RCA: 41] [Impact Index Per Article: 13.7] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/30/2021] [Revised: 05/10/2021] [Accepted: 06/04/2021] [Indexed: 02/05/2023]
Abstract
One important challenge in psychiatric research is to translate findings from brain imaging research studies that identified brain alterations in patient groups into an accurate diagnosis at an early stage of illness, prediction of prognosis before treatment, and guidance for selection of effective treatments that target patient-relevant pathophysiological features. This is the primary aim of the field of Psychoradiology. Using databases collected from large samples at multiple centers, sophisticated artificial intelligence (AI) algorithms may be used to develop clinically useful image analysis pipelines that can help physicians diagnose, predict, and make treatment decisions. In this review, we selectively summarize psychoradiological research using magnetic resonance imaging of the brain to explore the neural mechanism of psychiatric disorders, and outline progress and the path forward for the combination of psychoradiology and AI for complementing clinical examinations in patients with psychiatric disorders, as well as limitations in the application of AI that should be considered in future translational research.
Collapse
Affiliation(s)
- Fei Li
- Huaxi MR Research Center (HMRRC), Department of Radiology, West China Hospital of Sichuan University, Chengdu 610041, Sichuan, P.R. China
- Research Unit of Psychoradiology, Chinese Academy of Medical Sciences, Chengdu 610041, Sichuan, P.R. China
- Functional and Molecular Imaging Key Laboratory of Sichuan Provience, Department of Radiology, West China Hospital of Sichuan University, Chengdu 610041, Sichuan, P.R. China
| | - Huaiqiang Sun
- Huaxi MR Research Center (HMRRC), Department of Radiology, West China Hospital of Sichuan University, Chengdu 610041, Sichuan, P.R. China
- Research Unit of Psychoradiology, Chinese Academy of Medical Sciences, Chengdu 610041, Sichuan, P.R. China
- Functional and Molecular Imaging Key Laboratory of Sichuan Provience, Department of Radiology, West China Hospital of Sichuan University, Chengdu 610041, Sichuan, P.R. China
| | - Bharat B Biswal
- Department of Biomedical Engineering, New Jersey Institute of Technology, Newark, NJ 07102, USA
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Lab for Neuroinformation, University of Electronic Science and Technology of China, Chengdu 610054, Sichuan, P.R. China
| | - John A Sweeney
- Huaxi MR Research Center (HMRRC), Department of Radiology, West China Hospital of Sichuan University, Chengdu 610041, Sichuan, P.R. China
- Department of Psychiatry and Behavioral Neuroscience, University of Cincinnati, Cincinnati, OH 45219, USA
| | - Qiyong Gong
- Huaxi MR Research Center (HMRRC), Department of Radiology, West China Hospital of Sichuan University, Chengdu 610041, Sichuan, P.R. China
- Research Unit of Psychoradiology, Chinese Academy of Medical Sciences, Chengdu 610041, Sichuan, P.R. China
- Functional and Molecular Imaging Key Laboratory of Sichuan Provience, Department of Radiology, West China Hospital of Sichuan University, Chengdu 610041, Sichuan, P.R. China
| |
Collapse
|
19
|
Maziero MP, Seitz-Holland J, Cho KIK, Goldenberg JE, Tanamatis TW, Diniz JB, Cappi C, Alice de Mathis M, Otaduy MCG, da Graça Morais Martin M, de Melo Felipe da Silva R, Shavitt RG, Batistuzzo MC, Lopes AC, Miguel EC, Pasternak O, Hoexter MQ. Cellular and Extracellular White Matter Abnormalities in Obsessive-Compulsive Disorder: A Diffusion Magnetic Resonance Imaging Study. BIOLOGICAL PSYCHIATRY: COGNITIVE NEUROSCIENCE AND NEUROIMAGING 2021; 6:983-991. [PMID: 33862255 DOI: 10.1016/j.bpsc.2021.04.001] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/10/2021] [Revised: 03/17/2021] [Accepted: 04/02/2021] [Indexed: 10/21/2022]
Abstract
BACKGROUND While previous studies have implicated white matter (WM) as a core pathology of obsessive-compulsive disorder (OCD), the underlying neurobiological processes remain elusive. This study used free-water (FW) imaging derived from diffusion magnetic resonance imaging to identify cellular and extracellular WM abnormalities in patients with OCD compared with control subjects. Next, we investigated the association between diffusion measures and clinical variables in patients. METHODS We collected diffusion-weighted magnetic resonance imaging and clinical data from 83 patients with OCD (56 women/27 men, age 37.7 ± 10.6 years) and 52 control subjects (27 women/25 men, age 32.8 ± 11.5 years). Fractional anisotropy (FA), FA of cellular tissue, and extracellular FW maps were extracted and compared between patients and control subjects using tract-based spatial statistics and voxelwise comparison in FSL Randomise. Next, we correlated these WM measures with clinical variables (age of onset and symptom severity) and compared them between patients with and without comorbidities and patients with and without psychiatric medication. RESULTS Patients with OCD demonstrated lower FA (43.4% of the WM skeleton), lower FA of cellular tissue (31% of the WM skeleton), and higher FW (22.5% of the WM skeleton) compared with control subjects. We did not observe significant correlations between diffusion measures and clinical variables. Comorbidities and medication status did not influence diffusion measures. CONCLUSIONS Our findings of widespread FA, FA of cellular tissue, and FW abnormalities suggest that OCD is associated with microstructural cellular and extracellular abnormalities beyond the corticostriatothalamocortical circuits. Future multimodal longitudinal studies are needed to understand better the influence of essential clinical variables across the illness trajectory.
Collapse
Affiliation(s)
- Maria Paula Maziero
- Laboratório de Investigações Médicas 23, Instituto de Psiquiatria, Hospital das Clinicas Hospital das Clínicas da Faculdade de Medicina da Universidade de São Paulo, Faculdade de Medicina, Universidade de São Paulo, São Paulo, Brazil; Faculty of Medicine, City University of São Paulo, São Paulo, Brazil.
| | - Johanna Seitz-Holland
- Department of Psychiatry, Brigham and Women's Hospital, Harvard Medical School, Boston, Massachusetts
| | - Kang Ik K Cho
- Department of Psychiatry, Brigham and Women's Hospital, Harvard Medical School, Boston, Massachusetts
| | - Joshua E Goldenberg
- Department of Psychiatry, Brigham and Women's Hospital, Harvard Medical School, Boston, Massachusetts
| | - Taís W Tanamatis
- Laboratório de Investigações Médicas 23, Instituto de Psiquiatria, Hospital das Clinicas Hospital das Clínicas da Faculdade de Medicina da Universidade de São Paulo, Faculdade de Medicina, Universidade de São Paulo, São Paulo, Brazil
| | - Juliana B Diniz
- Laboratório de Investigações Médicas 23, Instituto de Psiquiatria, Hospital das Clinicas Hospital das Clínicas da Faculdade de Medicina da Universidade de São Paulo, Faculdade de Medicina, Universidade de São Paulo, São Paulo, Brazil
| | - Carolina Cappi
- Laboratório de Investigações Médicas 23, Instituto de Psiquiatria, Hospital das Clinicas Hospital das Clínicas da Faculdade de Medicina da Universidade de São Paulo, Faculdade de Medicina, Universidade de São Paulo, São Paulo, Brazil
| | - Maria Alice de Mathis
- Laboratório de Investigações Médicas 23, Instituto de Psiquiatria, Hospital das Clinicas Hospital das Clínicas da Faculdade de Medicina da Universidade de São Paulo, Faculdade de Medicina, Universidade de São Paulo, São Paulo, Brazil
| | - Maria C G Otaduy
- Laboratório de Investigações Médicas 44, Instituto de Radiologia, Hospital das Clinicas Hospital das Clínicas da Faculdade de Medicina da Universidade de São Paulo, Faculdade de Medicina, Universidade de São Paulo, São Paulo, Brazil
| | - Maria da Graça Morais Martin
- Laboratório de Investigações Médicas 44, Instituto de Radiologia, Hospital das Clinicas Hospital das Clínicas da Faculdade de Medicina da Universidade de São Paulo, Faculdade de Medicina, Universidade de São Paulo, São Paulo, Brazil
| | - Renata de Melo Felipe da Silva
- Laboratório de Investigações Médicas 23, Instituto de Psiquiatria, Hospital das Clinicas Hospital das Clínicas da Faculdade de Medicina da Universidade de São Paulo, Faculdade de Medicina, Universidade de São Paulo, São Paulo, Brazil
| | - Roseli G Shavitt
- Laboratório de Investigações Médicas 23, Instituto de Psiquiatria, Hospital das Clinicas Hospital das Clínicas da Faculdade de Medicina da Universidade de São Paulo, Faculdade de Medicina, Universidade de São Paulo, São Paulo, Brazil
| | - Marcelo C Batistuzzo
- Laboratório de Investigações Médicas 23, Instituto de Psiquiatria, Hospital das Clinicas Hospital das Clínicas da Faculdade de Medicina da Universidade de São Paulo, Faculdade de Medicina, Universidade de São Paulo, São Paulo, Brazil; Department of Methods and Techniques in Psychology, Humanities and Health Sciences School, Pontifical Catholic University of São Paulo, São Paulo, Brazil
| | - Antonio C Lopes
- Laboratório de Investigações Médicas 23, Instituto de Psiquiatria, Hospital das Clinicas Hospital das Clínicas da Faculdade de Medicina da Universidade de São Paulo, Faculdade de Medicina, Universidade de São Paulo, São Paulo, Brazil
| | - Eurípedes C Miguel
- Laboratório de Investigações Médicas 23, Instituto de Psiquiatria, Hospital das Clinicas Hospital das Clínicas da Faculdade de Medicina da Universidade de São Paulo, Faculdade de Medicina, Universidade de São Paulo, São Paulo, Brazil
| | - Ofer Pasternak
- Department of Psychiatry, Brigham and Women's Hospital, Harvard Medical School, Boston, Massachusetts; Department of Radiology, Brigham and Women's Hospital, Harvard Medical School, Boston, Massachusetts
| | - Marcelo Q Hoexter
- Laboratório de Investigações Médicas 23, Instituto de Psiquiatria, Hospital das Clinicas Hospital das Clínicas da Faculdade de Medicina da Universidade de São Paulo, Faculdade de Medicina, Universidade de São Paulo, São Paulo, Brazil.
| |
Collapse
|
20
|
Li L, Pan N, Zhang L, Lui S, Huang X, Xu X, Wang S, Lei D, Li L, Kemp GJ, Gong Q. Hippocampal subfield alterations in pediatric patients with post-traumatic stress disorder. Soc Cogn Affect Neurosci 2021; 16:334-344. [PMID: 33315100 PMCID: PMC7943370 DOI: 10.1093/scan/nsaa162] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/16/2020] [Revised: 10/15/2020] [Accepted: 12/14/2020] [Indexed: 02/05/2023] Open
Abstract
The hippocampus, a key structure with distinct subfield functions, is strongly implicated in the pathophysiology of post-traumatic stress disorder (PTSD); however, few studies of hippocampus subfields in PTSD have focused on pediatric patients. We therefore investigated the hippocampal subfield volume using an automated segmentation method and explored the subfield-centered functional connectivity aberrations related to the anatomical changes, in a homogenous population of traumatized children with and without PTSD. To investigate the potential diagnostic value in individual patients, we used a machine learning approach to identify features with significant discriminative power for diagnosis of PTSD using random forest classifiers. Compared to controls, we found significant mean volume reductions of 8.4% and 9.7% in the right presubiculum and hippocampal tail in patients, respectively. These two subfields' volumes were the most significant contributors to group discrimination, with a mean classification accuracy of 69% and a specificity of 81%. These anatomical alterations, along with the altered functional connectivity between (pre)subiculum and inferior frontal gyrus, may underlie deficits in fear circuitry leading to dysfunction of fear extinction and episodic memory, causally important in post-traumatic symptoms such as hypervigilance and re-experience. For the first time, we suggest that hippocampal subfield volumes might be useful in discriminating traumatized children with and without PTSD.
Collapse
Affiliation(s)
- Lei Li
- Huaxi MR Research Center (HMRRC), Department of Radiology, West China Hospital of Sichuan University, Chengdu 610041, China
| | - Nanfang Pan
- Huaxi MR Research Center (HMRRC), Department of Radiology, West China Hospital of Sichuan University, Chengdu 610041, China
| | - Lianqing Zhang
- Huaxi MR Research Center (HMRRC), Department of Radiology, West China Hospital of Sichuan University, Chengdu 610041, China
| | - Su Lui
- Huaxi MR Research Center (HMRRC), Department of Radiology, West China Hospital of Sichuan University, Chengdu 610041, China
| | - Xiaoqi Huang
- Huaxi MR Research Center (HMRRC), Department of Radiology, West China Hospital of Sichuan University, Chengdu 610041, China
| | - Xin Xu
- Huaxi MR Research Center (HMRRC), Department of Radiology, West China Hospital of Sichuan University, Chengdu 610041, China
| | - Song Wang
- Huaxi MR Research Center (HMRRC), Department of Radiology, West China Hospital of Sichuan University, Chengdu 610041, China
| | - Du Lei
- Department of Psychiatry and Behavioral Neuroscience, University of Cincinnati College of Medicine, Cincinnati, OH 45267, USA
| | - Lingjiang Li
- Mental Health Institute, The Second Xiangya Hospital of Central South University, Changsha 410008, China
| | - Graham J Kemp
- Liverpool Magnetic Resonance Imaging Centre and Institute of Life Course and Medical Sciences, University of Liverpool, Liverpool L693BX, UK
| | - Qiyong Gong
- Huaxi MR Research Center (HMRRC), Department of Radiology, West China Hospital of Sichuan University, Chengdu 610041, China
- Research Unit of Psychoradiology, Chinese Academy of Medical Sciences, Chengdu 610041, China
| |
Collapse
|
21
|
Luo L, Li Q, You W, Wang Y, Tang W, Li B, Yang Y, Sweeney JA, Li F, Gong Q. Altered brain functional network dynamics in obsessive-compulsive disorder. Hum Brain Mapp 2021; 42:2061-2076. [PMID: 33522660 PMCID: PMC8046074 DOI: 10.1002/hbm.25345] [Citation(s) in RCA: 30] [Impact Index Per Article: 10.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/15/2020] [Revised: 12/20/2020] [Accepted: 01/07/2021] [Indexed: 02/05/2023] Open
Abstract
Obsessive–compulsive disorder (OCD) is a debilitating and disabling neuropsychiatric disorder, whose neurobiological basis remains unclear. Although traditional static resting‐state magnetic resonance imaging (rfMRI) studies have found aberrant functional connectivity (FC) in OCD, alterations in whole‐brain FC and topological properties in the context of brain dynamics remain relatively unexplored. The rfMRI data of 29 patients with OCD and 40 healthy controls were analyzed using group independent component analysis to obtain independent components (ICs) and a sliding‐window approach to generate dynamic functional connectivity (dFC) matrices. dFC patterns were clustered into three reoccurring states, and state transition metrics were obtained. Then, graph‐theory methods were applied to dFC matrices to calculate the variability of network topological organization. The occurrence of a state (State 1) with the highest modularity index and lowest mean FC between networks was increased significantly in OCD, and the fractional time in brain State 1 was positively correlated with anxiety level in patients. State 1 was characterized by having positive connections within default mode (DMN) and salience networks (SAN), and negative coupling between the two networks. Additionally, ICs belonging to DMN and SAN showed lower temporal variability of nodal degree centrality and efficiency in patients, which was related to longer illness duration and higher current obsession ratings. Our results provide evidence of clinically relevant aberrant dynamic brain activity in OCD. Increased functional segregation among networks and impaired functional flexibility in connections among brain regions in DMN and SAN may play important roles in the neuropathology of OCD.
Collapse
Affiliation(s)
- Lekai Luo
- Huaxi MR Research Center (HMRRC), Department of Radiology, West China Hospital of Sichuan University, Chengdu, Sichuan, P.R. China.,Research Unit of Psychoradiology, Chinese Academy of Medical Sciences, Chengdu, Sichuan, P.R. China.,Functional and Molecular Imaging Key Laboratory of Sichuan Province, West China Hospital of Sichuan University, Chengdu, Sichuan, P.R. China
| | - Qian Li
- Huaxi MR Research Center (HMRRC), Department of Radiology, West China Hospital of Sichuan University, Chengdu, Sichuan, P.R. China.,Research Unit of Psychoradiology, Chinese Academy of Medical Sciences, Chengdu, Sichuan, P.R. China.,Functional and Molecular Imaging Key Laboratory of Sichuan Province, West China Hospital of Sichuan University, Chengdu, Sichuan, P.R. China
| | - Wanfang You
- Huaxi MR Research Center (HMRRC), Department of Radiology, West China Hospital of Sichuan University, Chengdu, Sichuan, P.R. China.,Research Unit of Psychoradiology, Chinese Academy of Medical Sciences, Chengdu, Sichuan, P.R. China.,Functional and Molecular Imaging Key Laboratory of Sichuan Province, West China Hospital of Sichuan University, Chengdu, Sichuan, P.R. China
| | - Yuxia Wang
- Huaxi MR Research Center (HMRRC), Department of Radiology, West China Hospital of Sichuan University, Chengdu, Sichuan, P.R. China.,Research Unit of Psychoradiology, Chinese Academy of Medical Sciences, Chengdu, Sichuan, P.R. China.,Functional and Molecular Imaging Key Laboratory of Sichuan Province, West China Hospital of Sichuan University, Chengdu, Sichuan, P.R. China
| | - Wanjie Tang
- Department of Psychiatry, West China Hospital of Sichuan University, Chengdu, Sichuan, P.R. China
| | - Bin Li
- Department of Psychiatry, West China Hospital of Sichuan University, Chengdu, Sichuan, P.R. China
| | - Yanchun Yang
- Department of Psychiatry, West China Hospital of Sichuan University, Chengdu, Sichuan, P.R. China
| | - John A Sweeney
- Huaxi MR Research Center (HMRRC), Department of Radiology, West China Hospital of Sichuan University, Chengdu, Sichuan, P.R. China.,Department of Psychiatry, University of Cincinnati, Cincinnati, Ohio, USA
| | - Fei Li
- Huaxi MR Research Center (HMRRC), Department of Radiology, West China Hospital of Sichuan University, Chengdu, Sichuan, P.R. China.,Research Unit of Psychoradiology, Chinese Academy of Medical Sciences, Chengdu, Sichuan, P.R. China.,Functional and Molecular Imaging Key Laboratory of Sichuan Province, West China Hospital of Sichuan University, Chengdu, Sichuan, P.R. China
| | - Qiyong Gong
- Huaxi MR Research Center (HMRRC), Department of Radiology, West China Hospital of Sichuan University, Chengdu, Sichuan, P.R. China.,Research Unit of Psychoradiology, Chinese Academy of Medical Sciences, Chengdu, Sichuan, P.R. China.,Functional and Molecular Imaging Key Laboratory of Sichuan Province, West China Hospital of Sichuan University, Chengdu, Sichuan, P.R. China
| |
Collapse
|
22
|
Translational application of neuroimaging in major depressive disorder: a review of psychoradiological studies. Front Med 2021; 15:528-540. [PMID: 33511554 DOI: 10.1007/s11684-020-0798-1] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/30/2019] [Accepted: 04/25/2020] [Indexed: 02/05/2023]
Abstract
Major depressive disorder (MDD) causes great decrements in health and quality of life with increments in healthcare costs, but the causes and pathogenesis of depression remain largely unknown, which greatly prevent its early detection and effective treatment. With the advancement of neuroimaging approaches, numerous functional and structural alterations in the brain have been detected in MDD and more recently attempts have been made to apply these findings to clinical practice. In this review, we provide an updated summary of the progress in translational application of psychoradiological findings in MDD with a specified focus on potential clinical usage. The foreseeable clinical applications for different MRI modalities were introduced according to their role in disorder classification, subtyping, and prediction. While evidence of cerebral structural and functional changes associated with MDD classification and subtyping was heterogeneous and/or sparse, the ACC and hippocampus have been consistently suggested to be important biomarkers in predicting treatment selection and treatment response. These findings underlined the potential utility of brain biomarkers for clinical practice.
Collapse
|
23
|
Szalisznyó K, Silverstein DN. Computational Predictions for OCD Pathophysiology and Treatment: A Review. Front Psychiatry 2021; 12:687062. [PMID: 34658945 PMCID: PMC8517225 DOI: 10.3389/fpsyt.2021.687062] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/28/2021] [Accepted: 06/01/2021] [Indexed: 01/29/2023] Open
Abstract
Obsessive compulsive disorder (OCD) can manifest as a debilitating disease with high degrees of co-morbidity as well as clinical and etiological heterogenity. However, the underlying pathophysiology is not clearly understood. Computational psychiatry is an emerging field in which behavior and its neural correlates are quantitatively analyzed and computational models are developed to improve understanding of disorders by comparing model predictions to observations. The aim is to more precisely understand psychiatric illnesses. Such computational and theoretical approaches may also enable more personalized treatments. Yet, these methodological approaches are not self-evident for clinicians with a traditional medical background. In this mini-review, we summarize a selection of computational OCD models and computational analysis frameworks, while also considering the model predictions from a perspective of possible personalized treatment. The reviewed computational approaches used dynamical systems frameworks or machine learning methods for modeling, analyzing and classifying patient data. Bayesian interpretations of probability for model selection were also included. The computational dissection of the underlying pathology is expected to narrow the explanatory gap between the phenomenological nosology and the neuropathophysiological background of this heterogeneous disorder. It may also contribute to develop biologically grounded and more informed dimensional taxonomies of psychopathology.
Collapse
Affiliation(s)
- Krisztina Szalisznyó
- Department of Neuroscience and Psychiatry, Uppsala University Hospital, Uppsala, Sweden.,Theoretical Neuroscience Group, Wigner Research Centre for Physics, Hungarian Academy of Sciences, Budapest, Hungary
| | | |
Collapse
|
24
|
Lee HJ, Kwon H, Kim JI, Lee JY, Lee JY, Bang S, Lee JM. The cingulum in very preterm infants relates to language and social-emotional impairment at 2 years of term-equivalent age. NEUROIMAGE-CLINICAL 2020; 29:102528. [PMID: 33338967 PMCID: PMC7750449 DOI: 10.1016/j.nicl.2020.102528] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 06/26/2020] [Revised: 10/15/2020] [Accepted: 12/04/2020] [Indexed: 01/25/2023]
Abstract
Maturation of specific WM tracts in preterm individuals differs from those of term controls. The elastic net logistic regression model was used to identify altered white matter tracts in the preterm brain. The alteration of the cingulum in the preterm at near-term correlate with neurodevelopmental scores at 18–22 months of age.
Background Relative to full-term infants, very preterm infants exhibit disrupted white matter (WM) maturation and problems related to development, including motor, cognitive, social-emotional, and receptive and expressive language processing. Objective The present study aimed to determine whether regional abnormalities in the WM microstructure of very preterm infants, as defined relative to those of full-term infants at a near-term age, are associated with neurodevelopmental outcomes at the age of 18–22 months. Methods We prospectively enrolled 89 very preterm infants (birth weight < 1500 g) and 43 normal full-term control infants born between 2016 and 2018. All infants underwent a structural brain magnetic resonance imaging scan at near-term age. The diffusion tensor imaging (DTI) metrics of the whole-brain WM tracts were extracted based on the neonatal probabilistic WM pathway. The elastic net logistic regression model was used to identify altered WM tracts in the preterm brain. We evaluated the associations between the altered WM microstructure at near-term age and motor, cognitive, social-emotional, and receptive and expressive language developments at 18–22 months of age, as measured using the Bayley Scales of Infant Development, Third Edition. Results We found that the elastic net logistic regression model could classify preterm and full-term neonates with an accuracy of 87.9% (corrected p < 0.008) using the DTI metrics in the pathway of interest with a 10% threshold level. The fractional anisotropy (FA) values of the body and splenium of the corpus callosum, middle cerebellar peduncle, left and right uncinate fasciculi, and right portion of the pathway between the premotor and primary motor cortices (premotor-PMC), as well as the mean axial diffusivity (AD) values of the left cingulum, were identified as contributive features for classification. Increased adjusted AD values in the left cingulum pathway were significantly correlated with language scores after false discovery rate (FDR) correction (r = 0.217, p = 0.043). The expressive language and social-emotional composite scores showed a significant positive correlation with the AD values in the left cingulum pathway (r = 0.226 [p = 0.036] and r = 0.31 [p = 0.003], respectively) after FDR correction. Conclusion Our approach suggests that the cingulum pathways of very preterm infants differ from those of full-term infants and significantly contribute to the prediction of the subsequent development of the language and social-emotional domains. This finding could improve our understanding of how specific neural substrates influence neurodevelopment at later ages, and individual risk prediction, thus helping to inform early intervention strategies that address developmental delay.
Collapse
Affiliation(s)
- Hyun Ju Lee
- Department of Pediatrics, Hanyang University College of Medicine, Seoul, South Korea; Division of Neonatology and Developmental Medicine, Seoul Hanyang University Hospital, Seoul, South Korea
| | - Hyeokjin Kwon
- Department of Electronic Engineering, Hanyang University, Seoul, South Korea
| | - Johanna Inhyang Kim
- Department of Psychiatry, Hanyang University, Seoul, South Korea; Division of Neonatology and Developmental Medicine, Seoul Hanyang University Hospital, Seoul, South Korea
| | - Joo Young Lee
- Department of Pediatrics, Hanyang University College of Medicine, Seoul, South Korea
| | - Ji Young Lee
- Department of Radiology, Hanyang University College of Medicine, Seoul, South Korea
| | - SungKyu Bang
- Department of Electronic Engineering, Hanyang University, Seoul, South Korea
| | - Jong-Min Lee
- Department of Biomedical Engineering, Hanyang University, Seoul, South Korea.
| |
Collapse
|
25
|
Huang J, Li Y, Xie H, Yang S, Jiang C, Sun W, Li D, Liao Y, Ba X, Xiao L. Abnormal Intrinsic Brain Activity and Neuroimaging-Based fMRI Classification in Patients With Herpes Zoster and Postherpetic Neuralgia. Front Neurol 2020; 11:532110. [PMID: 33192967 PMCID: PMC7642867 DOI: 10.3389/fneur.2020.532110] [Citation(s) in RCA: 9] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/08/2020] [Accepted: 09/01/2020] [Indexed: 01/20/2023] Open
Abstract
Objective: Neuroimaging studies on neuropathic pain have discovered abnormalities in brain structure and function. However, the brain pattern changes from herpes zoster (HZ) to postherpetic neuralgia (PHN) remain unclear. The present study aimed to compare the brain activity between HZ and PHN patients and explore the potential neural mechanisms underlying cognitive impairment in neuropathic pain patients. Methods: Resting-state functional magnetic resonance imaging (MRI) was carried out among 28 right-handed HZ patients, 24 right-handed PHN patients, and 20 healthy controls (HC), using a 3T MRI system. The amplitude of low-frequency fluctuation (ALFF) was analyzed to detect the brain activity of the patients. Correlations between ALFF and clinical pain scales were assessed in two groups of patients. Differences in brain activity between groups were examined and used in a support vector machine (SVM) algorithm for the subjects' classification. Results: Spontaneous brain activity was reduced in both patient groups. Compared with HC, patients from both groups had decreased ALFF in the precuneus, posterior cingulate cortex, and middle temporal gyrus. Meanwhile, the neural activities of angular gyrus and middle frontal gyrus were lowered in HZ and PHN patients, respectively. Reduced ALFF in these regions was associated with clinical pain scales in PHN patients only. Using SVM algorithm, the decreased brain activity in these regions allowed for the classification of neuropathic pain patients (HZ and PHN) and HC. Moreover, HZ and PHN patients are also roughly classified by the same model. Conclusion: Our study indicated that mean ALFF values in these pain-related regions can be used as a functional MRI-based biomarker for the classification of subjects with different pain conditions. Altered brain activity might contribute to PHN-induced pain.
Collapse
Affiliation(s)
- Jiabin Huang
- Department of Pain Medicine and Shenzhen Municipal Key Laboratory for Pain Medicine, Huazhong University of Science and Technology Union Shenzhen Hospital, Shenzhen, China
| | - Yongxin Li
- Formula-Pattern Research Center, School of Traditional Chinese Medicine, Jinan University, Guangzhou, China
| | - Huijun Xie
- Formula-Pattern Research Center, School of Traditional Chinese Medicine, Jinan University, Guangzhou, China
| | - Shaomin Yang
- Department of Pain Medicine and Shenzhen Municipal Key Laboratory for Pain Medicine, Huazhong University of Science and Technology Union Shenzhen Hospital, Shenzhen, China
| | - Changyu Jiang
- Department of Pain Medicine and Shenzhen Municipal Key Laboratory for Pain Medicine, Huazhong University of Science and Technology Union Shenzhen Hospital, Shenzhen, China
| | - Wuping Sun
- Department of Pain Medicine and Shenzhen Municipal Key Laboratory for Pain Medicine, Huazhong University of Science and Technology Union Shenzhen Hospital, Shenzhen, China
| | - Disen Li
- Department of Pain Medicine and Shenzhen Municipal Key Laboratory for Pain Medicine, Huazhong University of Science and Technology Union Shenzhen Hospital, Shenzhen, China
| | - Yuliang Liao
- Department of Pain Medicine and Shenzhen Municipal Key Laboratory for Pain Medicine, Huazhong University of Science and Technology Union Shenzhen Hospital, Shenzhen, China
| | - Xiyuan Ba
- Department of Pain Medicine and Shenzhen Municipal Key Laboratory for Pain Medicine, Huazhong University of Science and Technology Union Shenzhen Hospital, Shenzhen, China
| | - Lizu Xiao
- Department of Pain Medicine and Shenzhen Municipal Key Laboratory for Pain Medicine, Huazhong University of Science and Technology Union Shenzhen Hospital, Shenzhen, China
| |
Collapse
|
26
|
Rashid B, Calhoun V. Towards a brain-based predictome of mental illness. Hum Brain Mapp 2020; 41:3468-3535. [PMID: 32374075 PMCID: PMC7375108 DOI: 10.1002/hbm.25013] [Citation(s) in RCA: 65] [Impact Index Per Article: 16.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/21/2019] [Revised: 04/06/2020] [Accepted: 04/06/2020] [Indexed: 01/10/2023] Open
Abstract
Neuroimaging-based approaches have been extensively applied to study mental illness in recent years and have deepened our understanding of both cognitively healthy and disordered brain structure and function. Recent advancements in machine learning techniques have shown promising outcomes for individualized prediction and characterization of patients with psychiatric disorders. Studies have utilized features from a variety of neuroimaging modalities, including structural, functional, and diffusion magnetic resonance imaging data, as well as jointly estimated features from multiple modalities, to assess patients with heterogeneous mental disorders, such as schizophrenia and autism. We use the term "predictome" to describe the use of multivariate brain network features from one or more neuroimaging modalities to predict mental illness. In the predictome, multiple brain network-based features (either from the same modality or multiple modalities) are incorporated into a predictive model to jointly estimate features that are unique to a disorder and predict subjects accordingly. To date, more than 650 studies have been published on subject-level prediction focusing on psychiatric disorders. We have surveyed about 250 studies including schizophrenia, major depression, bipolar disorder, autism spectrum disorder, attention-deficit hyperactivity disorder, obsessive-compulsive disorder, social anxiety disorder, posttraumatic stress disorder, and substance dependence. In this review, we present a comprehensive review of recent neuroimaging-based predictomic approaches, current trends, and common shortcomings and share our vision for future directions.
Collapse
Affiliation(s)
- Barnaly Rashid
- Department of PsychiatryHarvard Medical SchoolBostonMassachusettsUSA
| | - Vince Calhoun
- Tri‐Institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS)Georgia State University, Georgia Institute of Technology, and Emory UniversityAtlantaGeorgiaUSA
| |
Collapse
|
27
|
Hu X, Zhang L, Bu X, Li H, Gao Y, Lu L, Tang S, Wang Y, Huang X, Gong Q. White matter disruption in obsessive-compulsive disorder revealed by meta-analysis of tract-based spatial statistics. Depress Anxiety 2020; 37:620-631. [PMID: 32275111 DOI: 10.1002/da.23008] [Citation(s) in RCA: 23] [Impact Index Per Article: 5.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/09/2019] [Revised: 02/29/2020] [Accepted: 03/10/2020] [Indexed: 02/05/2023] Open
Abstract
BACKGROUND Exploring white matter (WM) microstructural alterations is a momentous step for gaining insights about underlying mechanisms of obsessive-compulsive disorder (OCD) and improving the efficacy of therapies for this condition. Many tract-based spatial statistics (TBSS) studies have revealed abnormalities of fractional anisotropy (FA; an index of WM integrity) in OCD. However, research works have not drawn robust conclusions. Therefore, we integrated the findings of TBSS studies to identify the most consistent FA changes in OCD using meta-analytical approach. METHODS Online databases were systematically searched for all TBSS studies comparing FA between patients with OCD and controls. A coordinate-based meta-analysis was performed using anisotropic effect size version of the seed-based d mapping software. Meanwhile, meta-regression was used to explore the potential association of clinical characteristics with regional FA abnormalities. RESULTS Our meta-analysis included 488 OCD patients and 519 controls across 17 datasets. FA reductions were identified in the genu of the corpus callosum and the left orbitofrontal WM in OCD patients relative to controls. Metaregression analyses showed that the FA in the left orbitofrontal WM was negatively and independently correlated with symptom severity and illness duration in patients with OCD. CONCLUSIONS The current study provides a quantitative overview of TBSS findings in OCD and demonstrates the most prominent and replicable WM abnormalities in OCD are in the anterior part of the brain including interhemispheric connection and orbitofrontal region. Additionally, our findings suggest that FA reduction in the orbitofrontal WM might be a potential biomarker in predicting disease severity and progression in patients with OCD.
Collapse
Affiliation(s)
- Xinyu Hu
- Huaxi MR Research Center (HMRRC), Department of Radiology, West China Hospital of Sichuan University, Chengdu, Sichuan, China
| | - Lianqing Zhang
- Huaxi MR Research Center (HMRRC), Department of Radiology, West China Hospital of Sichuan University, Chengdu, Sichuan, China
| | - Xuan Bu
- Huaxi MR Research Center (HMRRC), Department of Radiology, West China Hospital of Sichuan University, Chengdu, Sichuan, China
| | - Hailong Li
- Huaxi MR Research Center (HMRRC), Department of Radiology, West China Hospital of Sichuan University, Chengdu, Sichuan, China
| | - Yingxue Gao
- Huaxi MR Research Center (HMRRC), Department of Radiology, West China Hospital of Sichuan University, Chengdu, Sichuan, China
| | - Lu Lu
- Huaxi MR Research Center (HMRRC), Department of Radiology, West China Hospital of Sichuan University, Chengdu, Sichuan, China
| | - Shi Tang
- Huaxi MR Research Center (HMRRC), Department of Radiology, West China Hospital of Sichuan University, Chengdu, Sichuan, China
| | - Yanlin Wang
- Huaxi MR Research Center (HMRRC), Department of Radiology, West China Hospital of Sichuan University, Chengdu, Sichuan, China
| | - Xiaoqi Huang
- Huaxi MR Research Center (HMRRC), Department of Radiology, West China Hospital of Sichuan University, Chengdu, Sichuan, China.,Psychoradiology Research Unit of the Chinese Academy of Medical Sciences (2018RU011), West China Hospital of Sichuan University, Chengdu, Sichuan, China
| | - Qiyong Gong
- Huaxi MR Research Center (HMRRC), Department of Radiology, West China Hospital of Sichuan University, Chengdu, Sichuan, China.,Psychoradiology Research Unit of the Chinese Academy of Medical Sciences (2018RU011), West China Hospital of Sichuan University, Chengdu, Sichuan, China
| |
Collapse
|
28
|
Cyr M, Pagliaccio D, Yanes-Lukin P, Fontaine M, Rynn MA, Marsh R. Altered network connectivity predicts response to cognitive-behavioral therapy in pediatric obsessive-compulsive disorder. Neuropsychopharmacology 2020; 45:1232-1240. [PMID: 31952071 PMCID: PMC7235012 DOI: 10.1038/s41386-020-0613-3] [Citation(s) in RCA: 21] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/12/2019] [Revised: 11/21/2019] [Accepted: 01/08/2020] [Indexed: 12/12/2022]
Abstract
Obsessive-compulsive disorder (OCD) is commonly associated with alterations in cortico-striato-thalamo-cortical brain networks. Yet, recent investigations of large-scale brain networks suggest that more diffuse alterations in brain connectivity may underlie its pathophysiology. Few studies have assessed functional connectivity within or between networks across the whole brain in pediatric OCD or how patterns of connectivity associate with treatment response. Resting-state functional magnetic resonance imaging scans were acquired from 25 unmedicated, treatment-naive children and adolescents with OCD (12.8 ± 2.9 years) and 23 matched healthy control (HC) participants (11.0 ± 3.3 years) before participants with OCD completed a course of cognitive-behavioral therapy (CBT). Participants were re-scanned after 12-16 weeks. Whole-brain connectomic analyses were conducted to assess baseline group differences and group-by-time interactions, corrected for multiple comparisons. Relationships between functional connectivity and OCD symptoms pre- and post-CBT were examined using longitudinal cross-lagged panel modeling. Reduced connectivity in OCD relative to HC participants was detected between default mode and task-positive network regions. Greater (less altered) connectivity between left angular gyrus and left frontal pole predicted better response to CBT in the OCD group. Altered connectivity between task-positive and task-negative networks in pediatric OCD may contribute to the impaired control over intrusive thoughts early in the illness. This is the first study to show that altered connectivity between large-scale network regions may predict response to CBT in pediatric OCD, highlighting the clinical relevance of these networks as potential circuit-based targets for the development of novel treatments.
Collapse
Affiliation(s)
- Marilyn Cyr
- Division of Child and Adolescent Psychiatry, New York State Psychiatric Institute, New York, NY, USA. .,Department of Psychiatry, Vagelos College of Physicians and Surgeons, Columbia University, New York, NY, USA.
| | - David Pagliaccio
- grid.413734.60000 0000 8499 1112Division of Child and Adolescent Psychiatry, New York State Psychiatric Institute, New York, NY USA ,grid.21729.3f0000000419368729Department of Psychiatry, Vagelos College of Physicians and Surgeons, Columbia University, New York, NY USA
| | - Paula Yanes-Lukin
- grid.413734.60000 0000 8499 1112Division of Child and Adolescent Psychiatry, New York State Psychiatric Institute, New York, NY USA ,grid.21729.3f0000000419368729Department of Psychiatry, Vagelos College of Physicians and Surgeons, Columbia University, New York, NY USA
| | - Martine Fontaine
- grid.413734.60000 0000 8499 1112Division of Child and Adolescent Psychiatry, New York State Psychiatric Institute, New York, NY USA ,grid.21729.3f0000000419368729Department of Psychiatry, Vagelos College of Physicians and Surgeons, Columbia University, New York, NY USA
| | - Moira A. Rynn
- grid.26009.3d0000 0004 1936 7961Department of Psychiatry & Behavioral Sciences, Duke University School of Medicine, Durham, NC USA
| | - Rachel Marsh
- grid.413734.60000 0000 8499 1112Division of Child and Adolescent Psychiatry, New York State Psychiatric Institute, New York, NY USA ,grid.21729.3f0000000419368729Department of Psychiatry, Vagelos College of Physicians and Surgeons, Columbia University, New York, NY USA
| |
Collapse
|
29
|
Chen QF, Zhang XH, Huang NX, Chen HJ. Identification of Amyotrophic Lateral Sclerosis Based on Diffusion Tensor Imaging and Support Vector Machine. Front Neurol 2020; 11:275. [PMID: 32411072 PMCID: PMC7198809 DOI: 10.3389/fneur.2020.00275] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/15/2020] [Accepted: 03/24/2020] [Indexed: 11/13/2022] Open
Abstract
Objectives: White matter (WM) impairments involving both motor and extra-motor areas have been well-documented in amyotrophic lateral sclerosis (ALS). This study tested the potential of diffusion measurements in WM for identifying ALS based on support vector machine (SVM). Methods: Voxel-wise fractional anisotropy (FA) values of diffusion tensor images (DTI) were extracted from 22 ALS patients and 26 healthy controls and served as discrimination features. The revised ALS Functional Rating Scale (ALSFRS-R) was employed to assess ALS severity. Feature ranking and selection were based on Fisher scores. A linear kernel SVM algorithm was applied to build the classification model, from which the classification performance was evaluated. To promote classifier generalization ability, a leave-one-out cross-validation (LOOCV) method was adopted. Results: By using the 2,400~3,400 ranked features as optimal features, the highest classification accuracy of 83.33% (sensitivity = 77.27% and specificity = 88.46%, P = 0.0001) was achieved, with an area under receiver operating characteristic curve of 0.862. The predicted function value was positively correlated with patient ALSFRS-R scores (r = 0.493, P = 0.020). In the optimized SVM model, FA values from several regions mostly contributed to classification, primarily involving the corticospinal tract pathway, postcentral gyrus, and frontal and parietal areas. Conclusions: Our results suggest the feasibility of ALS diagnosis based on SVM analysis and diffusion measurements of WM. Additional investigations using a larger cohort is recommended in order to validate the results of this study.
Collapse
Affiliation(s)
- Qiu-Feng Chen
- College of Computer and Information Sciences, Fujian Agriculture and Forestry University, Fuzhou, China
| | - Xiao-Hong Zhang
- Department of Radiology, Fujian Medical University Union Hospital, Fuzhou, China
| | - Nao-Xin Huang
- Department of Radiology, Fujian Medical University Union Hospital, Fuzhou, China
| | - Hua-Jun Chen
- Department of Radiology, Fujian Medical University Union Hospital, Fuzhou, China
| |
Collapse
|
30
|
Chen QF, Zou TX, Yang ZT, Chen HJ. Identification of patients with and without minimal hepatic encephalopathy based on gray matter volumetry using a support vector machine learning algorithm. Sci Rep 2020; 10:2490. [PMID: 32051514 PMCID: PMC7016173 DOI: 10.1038/s41598-020-59433-1] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/06/2019] [Accepted: 01/29/2020] [Indexed: 12/13/2022] Open
Abstract
Minimal hepatic encephalopathy (MHE) is characterized by diffuse abnormalities in cerebral structure, such as reduced cortical thickness and altered brain parenchymal volume. This study tested the potential of gray matter (GM) volumetry to differentiate between cirrhotic patients with and without MHE using a support vector machine (SVM) learning method. High-resolution, T1-weighted magnetic resonance images were acquired from 24 cirrhotic patients with MHE and 29 cirrhotic patients without MHE (NHE). Voxel-based morphometry was conducted to evaluate the GM volume (GMV) for each subject. An SVM classifier was employed to explore the ability of the GMV measurement to diagnose MHE, and the leave-one-out cross-validation method was used to assess classification accuracy. The SVM algorithm based on GM volumetry achieved a classification accuracy of 83.02%, with a sensitivity of 83.33% and a specificity of 82.76%. The majority of the most discriminative GMVs were located in the bilateral frontal lobe, bilateral lentiform nucleus, bilateral thalamus, bilateral sensorimotor areas, bilateral visual regions, bilateral temporal lobe, bilateral cerebellum, left inferior parietal lobe, and right precuneus/posterior cingulate gyrus. Our results suggest that SVM analysis based on GM volumetry has the potential to help diagnose MHE in cirrhotic patients.
Collapse
Affiliation(s)
- Qiu-Feng Chen
- College of Computer and Information Sciences, Fujian Agriculture and Forestry University, Fuzhou, 350002, China
| | - Tian-Xiu Zou
- Department of Radiology, Fujian Medical University Union Hospital, Fuzhou, 350001, China
| | - Zhe-Ting Yang
- Department of Radiology, Fujian Medical University Union Hospital, Fuzhou, 350001, China
| | - Hua-Jun Chen
- Department of Radiology, Fujian Medical University Union Hospital, Fuzhou, 350001, China.
| |
Collapse
|
31
|
Li Y, Tan Z, Wang Y, Wang Y, Li D, Chen Q, Huang W. Detection of differentiated changes in gray matter in children with progressive hydrocephalus and chronic compensated hydrocephalus using voxel-based morphometry and machine learning. Anat Rec (Hoboken) 2019; 303:2235-2247. [PMID: 31654555 DOI: 10.1002/ar.24306] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/19/2018] [Revised: 08/31/2019] [Accepted: 09/22/2019] [Indexed: 12/22/2022]
Abstract
Currently, no neuroimaging study has reported the detection of specific imaging biomarkers that distinguish the progressive hydrocephalus (PH) and chronic compensated hydrocephalus (CH). Our main focus is to evaluate the different structural changes in classifying the two types of hydrocephalus children. Twenty-two children with hydrocephalus (12 PHs and 10 CHs) and 30 age-matched healthy controls were enrolled and the T1-weighted imaging was collected in the study. A customized voxel-based morphometry (VBM) approach and support vector machine (SVM) were combined to investigate the structural changes and group classification. Comparing with the controls and CH, PH groups invariably showed a significant decrease of GM volume in the bilateral hippocampus/parahippocampus, insula, and motor-related areas. SVM applied to the GM volumes of bilateral hippocampus/parahippocampus, insula, and motor-related areas correctly identified hydrocephalus children from normal controls with a statistically significant accuracy of 88.46% (p ≤ .001). In addition, SVM applied to GM volumes of the same regions correctly identified PH from CH with a statistically significant accuracy of 77.27% (p ≤ .009). Using VBM analysis, we characterized and visualized the GM changes in children with hydrocephalus. Machine learning results further confirmed that a significant decrease of the bilateral hippocampus/parahippocampus, insula, and motor-related GM volume can serve as a specific neuroimaging index to distinguish the children with PH from the children with CH and controls at individual. The findings could help to aid the identification of individuals with PH in clinical practice.
Collapse
Affiliation(s)
- Yongxin Li
- Formula-pattern Research Center, School of Traditional Chinese Medicine, Jinan University, Guangzhou, China
| | - Zhen Tan
- Health Management Center, Shenzhen University General Hospital, Shenzhen University Clinical Medical Academy, Shenzhen, China
| | - Ya Wang
- Guangdong Provincial Key Laboratory of Medical Biomechanics, School of Basic Medical Sciences, Southern Medical University, Guangzhou, China
| | - Yanfang Wang
- Guangdong Provincial Key Laboratory of Medical Biomechanics, School of Basic Medical Sciences, Southern Medical University, Guangzhou, China
| | - Ding Li
- Guangdong Provincial Key Laboratory of Medical Biomechanics, School of Basic Medical Sciences, Southern Medical University, Guangzhou, China
| | - Qian Chen
- Department of Pediatric Neurosurgery, Shenzhen Children's Hospital, Shenzhen, China
| | - Wenhua Huang
- Guangdong Provincial Key Laboratory of Medical Biomechanics, School of Basic Medical Sciences, Southern Medical University, Guangzhou, China
| |
Collapse
|
32
|
Huang X, Gong Q, Sweeney JA, Biswal BB. Progress in psychoradiology, the clinical application of psychiatric neuroimaging. Br J Radiol 2019; 92:20181000. [PMID: 31170803 PMCID: PMC6732936 DOI: 10.1259/bjr.20181000] [Citation(s) in RCA: 42] [Impact Index Per Article: 8.4] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/26/2018] [Revised: 05/09/2019] [Accepted: 05/21/2019] [Indexed: 02/05/2023] Open
Abstract
Psychoradiology is an emerging field that applies radiological imaging technologies to psychiatric conditions. In the past three decades, brain imaging techniques have rapidly advanced understanding of illness and treatment effects in psychiatry. Based on these advances, radiologists have become increasingly interested in applying these advances for differential diagnosis and individualized patient care selection for common psychiatric illnesses. This shift from research to clinical practice represents the beginning evolution of psychoradiology. In this review, we provide a summary of recent progress relevant to this field based on their clinical functions, namely the (1) classification and subtyping; (2) prediction and monitoring of treatment outcomes; and (3) treatment selection. In addition, we provide guidelines for the practice of psychoradiology in clinical settings and suggestions for future research to validate broader clinical applications. Given the high prevalence of psychiatric disorders and the importance of increased participation of radiologists in this field, a guide regarding advances in this field and a description of relevant clinical work flow patterns help radiologists contribute to this fast-evolving field.
Collapse
Affiliation(s)
| | | | - John A. Sweeney
- Department of Psychiatry and Behavioral Neuroscience, University of Cincinnati, Cincinnati, USA
| | | |
Collapse
|
33
|
Yang X, Hu X, Tang W, Li B, Yang Y, Gong Q, Huang X. Multivariate classification of drug-naive obsessive-compulsive disorder patients and healthy controls by applying an SVM to resting-state functional MRI data. BMC Psychiatry 2019; 19:210. [PMID: 31277632 PMCID: PMC6612132 DOI: 10.1186/s12888-019-2184-6] [Citation(s) in RCA: 21] [Impact Index Per Article: 4.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/24/2018] [Accepted: 06/13/2019] [Indexed: 02/05/2023] Open
Abstract
BACKGROUND Previous resting-state functional magnetic resonance imaging (rs-fMRI) studies have revealed intrinsic regional activity alterations in obsessive-compulsive disorder (OCD), but those results were based on group analyses, which limits their applicability to clinical diagnosis and treatment at the level of the individual. METHODS We examined fractional amplitude low-frequency fluctuation (fALFF) and applied support vector machine (SVM) to discriminate OCD patients from healthy controls on the basis of rs-fMRI data. Values of fALFF, calculated from 68 drug-naive OCD patients and 68 demographically matched healthy controls, served as input features for the classification procedure. RESULTS The classifier achieved 72% accuracy (p ≤ 0.001). This discrimination was based on regions that included the left superior temporal gyrus, the right middle temporal gyrus, the left supramarginal gyrus and the superior parietal lobule. CONCLUSIONS These results indicate that OCD-related abnormalities in temporal and parietal lobe activation have predictive power for group membership; furthermore, the findings suggest that machine learning techniques can be used to aid in the identification of individuals with OCD in clinical diagnosis.
Collapse
Affiliation(s)
- Xi Yang
- 0000 0004 1770 1022grid.412901.fMental Health Center Department of Psychiatry, West China Hospital Sichuan University, Chengdu, China ,Shenzhen Mental Health Center, Shenzhen, China
| | - Xinyu Hu
- 0000 0004 1770 1022grid.412901.fHuaxi MR Research Center (HMRRC) Department of Radiology, West China Hospital Sichuan University, Chengdu, 610041 China
| | - Wanjie Tang
- 0000 0004 1770 1022grid.412901.fMental Health Center Department of Psychiatry, West China Hospital Sichuan University, Chengdu, China
| | - Bin Li
- 0000 0004 1770 1022grid.412901.fMental Health Center Department of Psychiatry, West China Hospital Sichuan University, Chengdu, China
| | - Yanchun Yang
- Mental Health Center Department of Psychiatry, West China Hospital Sichuan University, Chengdu, China.
| | - Qiyong Gong
- Huaxi MR Research Center (HMRRC) Department of Radiology, West China Hospital Sichuan University, Chengdu, 610041, China.
| | - Xiaoqi Huang
- Huaxi MR Research Center (HMRRC) Department of Radiology, West China Hospital Sichuan University, Chengdu, 610041, China.
| |
Collapse
|
34
|
Hu X, Zhang L, Bu X, Li H, Li B, Tang W, Lu L, Hu X, Tang S, Gao Y, Yang Y, Roberts N, Gong Q, Huang X. Localized Connectivity in Obsessive-Compulsive Disorder: An Investigation Combining Univariate and Multivariate Pattern Analyses. Front Behav Neurosci 2019; 13:122. [PMID: 31249515 PMCID: PMC6584748 DOI: 10.3389/fnbeh.2019.00122] [Citation(s) in RCA: 19] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/28/2018] [Accepted: 05/20/2019] [Indexed: 02/05/2023] Open
Abstract
Recent developments in psychoradiological researches have highlighted the disrupted organization of large-scale functional brain networks in obsessive-compulsive disorder (OCD). However, whether abnormal activation of localized brain areas would affect network dysfunction remains to be fully characterized. We applied both univariate analysis and multivariate pattern analysis (MVPA) approaches to investigate the abnormalities of regional homogeneity (ReHo), an index to measure the localized connectivity, in 88 medication-free patients with OCD and 88 healthy control subjects (HCS). Resting-state functional magnetic resonance imaging (RS-fMRI) data of all the participants were acquired in a 3.0-T scanner. First, we adopted a traditional univariate analysis to explore ReHo alterations between the patient group and the control group. Subsequently, we utilized a support vector machine (SVM) to examine whether ReHo could be further used to differentiate patients with OCD from HCS at the individual level. Relative to HCS, OCD patients showed lower ReHo in the bilateral cerebellum and higher ReHo in the bilateral superior frontal gyri (SFG), right inferior parietal gyrus (IPG), and precuneus [P < 0.05, family-wise error (FWE) correction]. ReHo value in the left SFG positively correlated with Yale-Brown Obsessive Compulsive Scale (Y-BOCS) total score (r = 0 0.241, P = 0.024) and obsessive subscale (r = 0.224, P = 0.036). The SVM classification regarding ReHo yielded an accuracy of 78.98% (sensitivity = 78.41%, specificity = 79.55%) with P < 0.001 after permutation testing. The most discriminative regions contributing to the SVM classification were mainly located in the frontal, temporal, and parietal regions as well as in the cerebellum while the right orbital frontal cortex was identified with the highest discriminative power. Our findings not only suggested that the localized activation disequilibrium between the prefrontal cortex (PFC) and the cerebellum appeared to be associated with the pathophysiology of OCD but also indicated the translational role of the localized connectivity as a potential discriminative pattern to detect OCD at the individual level.
Collapse
Affiliation(s)
- Xinyu Hu
- Huaxi MR Research Center (HMRRC), West China Hospital of Sichuan University, Chengdu, China
| | - Lianqing Zhang
- Huaxi MR Research Center (HMRRC), West China Hospital of Sichuan University, Chengdu, China
| | - Xuan Bu
- Huaxi MR Research Center (HMRRC), West China Hospital of Sichuan University, Chengdu, China
| | - Hailong Li
- Huaxi MR Research Center (HMRRC), West China Hospital of Sichuan University, Chengdu, China
| | - Bin Li
- Mental Health Center, West China Hospital of Sichuan University, Chengdu, China
| | - Wanjie Tang
- Mental Health Center, West China Hospital of Sichuan University, Chengdu, China
| | - Lu Lu
- Huaxi MR Research Center (HMRRC), West China Hospital of Sichuan University, Chengdu, China
| | - Xiaoxiao Hu
- Huaxi MR Research Center (HMRRC), West China Hospital of Sichuan University, Chengdu, China
| | - Shi Tang
- Huaxi MR Research Center (HMRRC), West China Hospital of Sichuan University, Chengdu, China
| | - Yingxue Gao
- Huaxi MR Research Center (HMRRC), West China Hospital of Sichuan University, Chengdu, China
| | - Yanchun Yang
- Mental Health Center, West China Hospital of Sichuan University, Chengdu, China
| | - Neil Roberts
- Clinical Research Imaging Centre (CRIC), The Queen's Medical Research Institute (QMRI), University of Edinburgh, Edinburgh, United Kingdom
| | - Qiyong Gong
- Huaxi MR Research Center (HMRRC), West China Hospital of Sichuan University, Chengdu, China
| | - Xiaoqi Huang
- Huaxi MR Research Center (HMRRC), West China Hospital of Sichuan University, Chengdu, China
| |
Collapse
|
35
|
Bruin W, Denys D, van Wingen G. Diagnostic neuroimaging markers of obsessive-compulsive disorder: Initial evidence from structural and functional MRI studies. Prog Neuropsychopharmacol Biol Psychiatry 2019; 91:49-59. [PMID: 30107192 DOI: 10.1016/j.pnpbp.2018.08.005] [Citation(s) in RCA: 28] [Impact Index Per Article: 5.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/24/2018] [Revised: 07/30/2018] [Accepted: 08/09/2018] [Indexed: 01/09/2023]
Abstract
As of yet, no diagnostic biomarkers are available for obsessive-compulsive disorder (OCD), and its diagnosis relies entirely upon the recognition of behavioural features assessed through clinical interview. Neuroimaging studies have shown that various brain structures are abnormal in OCD patients compared to healthy controls. However, the majority of these results are based on average differences between groups, which limits diagnostic usage in clinical practice. In recent years, a growing number of studies have applied multivariate pattern analysis (MVPA) techniques on neuroimaging data to extract patterns of altered brain structure, function and connectivity typical for OCD. MVPA techniques can be used to develop predictive models that extract regularities in data to classify individual subjects based on their diagnosis. In the present paper, we reviewed the literature of MVPA studies using data from different imaging modalities to distinguish OCD patients from controls. A systematic search retrieved twelve articles that fulfilled the inclusion and exclusion criteria. Reviewed studies have been able to classify OCD diagnosis with accuracies ranging from 66% up to 100%. Features important for classification were different across imaging modalities and widespread throughout the brain. Although studies have shown promising results, sample sizes used are typically small which can lead to high variance of the estimated model accuracy, cohort-specific solutions and lack of generalizability of findings. Some of the challenges are discussed that need to be overcome in order to move forward toward clinical applications.
Collapse
Affiliation(s)
- Willem Bruin
- Department of Psychiatry, Amsterdam UMC, University of Amsterdam, Amsterdam, the Netherlands; Amsterdam Neuroscience, Amsterdam UMC, University of Amsterdam, Amsterdam, the Netherlands; Amsterdam Brain and Cognition, University of Amsterdam, Amsterdam, the Netherlands.
| | - Damiaan Denys
- Department of Psychiatry, Amsterdam UMC, University of Amsterdam, Amsterdam, the Netherlands; Amsterdam Neuroscience, Amsterdam UMC, University of Amsterdam, Amsterdam, the Netherlands; Amsterdam Brain and Cognition, University of Amsterdam, Amsterdam, the Netherlands
| | - Guido van Wingen
- Department of Psychiatry, Amsterdam UMC, University of Amsterdam, Amsterdam, the Netherlands; Amsterdam Neuroscience, Amsterdam UMC, University of Amsterdam, Amsterdam, the Netherlands; Amsterdam Brain and Cognition, University of Amsterdam, Amsterdam, the Netherlands
| |
Collapse
|
36
|
Bu X, Hu X, Zhang L, Li B, Zhou M, Lu L, Hu X, Li H, Yang Y, Tang W, Gong Q, Huang X. Investigating the predictive value of different resting-state functional MRI parameters in obsessive-compulsive disorder. Transl Psychiatry 2019; 9:17. [PMID: 30655506 PMCID: PMC6336781 DOI: 10.1038/s41398-018-0362-9] [Citation(s) in RCA: 49] [Impact Index Per Article: 9.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/12/2018] [Revised: 10/26/2018] [Accepted: 12/09/2018] [Indexed: 02/05/2023] Open
Abstract
Previous resting-state functional magnetic resonance imaging (rs-fMRI) studies of obsessive-compulsive disorder (OCD) have facilitated our understanding of OCD pathophysiology based on its intrinsic activity. However, whether the group difference derived from univariate analysis could be useful for informing the diagnosis of individual OCD patients remains unclear. We aimed to apply multivariate pattern analysis of different rs-fMRI parameters to distinguish drug-naive patients with OCD from healthy control subjects (HCS). Fifty-four drug-naive OCD patients and 54 well-matched HCS were recruited. Four different rs-fMRI parameter maps, including the amplitude of low-frequency fluctuations (ALFF), fractional amplitude of low-frequency fluctuations (fALFF), regional homogeneity (ReHo) and functional connectivity strength (FCS), were calculated. Training of a support vector machine (SVM) classifier using rs-fMRI maps produced voxelwise discrimination maps. Overall, the classification accuracies were acceptable for the four rs-fMRI parameters. Excellent performance was achieved when ALFF maps were employed (accuracy, 95.37%, p < 0.01), good performance was achieved by using ReHo maps, weaker performance was achieved by using fALFF maps, and fair performance was achieved by using FCS maps. The brain regions showing the greatest discriminative power included the prefrontal cortex, anterior cingulate cortex, precentral gyrus, and occipital lobes. The application of SVM to rs-fMRI features may provide potential power for OCD classification.
Collapse
Affiliation(s)
- Xuan Bu
- 0000 0004 1770 1022grid.412901.fHuaxi MR Research Center (HMRRC), Department of Radiology, West China Hospital of Sichuan University, 37 Guo Xue Xiang, Chengdu, Sichuan 610041 China
| | - Xinyu Hu
- 0000 0004 1770 1022grid.412901.fHuaxi MR Research Center (HMRRC), Department of Radiology, West China Hospital of Sichuan University, 37 Guo Xue Xiang, Chengdu, Sichuan 610041 China
| | - Lianqing Zhang
- 0000 0004 1770 1022grid.412901.fHuaxi MR Research Center (HMRRC), Department of Radiology, West China Hospital of Sichuan University, 37 Guo Xue Xiang, Chengdu, Sichuan 610041 China
| | - Bin Li
- 0000 0004 1770 1022grid.412901.fMental Health Center, Department of Psychiatry, West China Hospital of Sichuan University, 37 Guo Xue Xiang, Chengdu, Sichuan 610041 China
| | - Ming Zhou
- 0000 0004 1770 1022grid.412901.fHuaxi MR Research Center (HMRRC), Department of Radiology, West China Hospital of Sichuan University, 37 Guo Xue Xiang, Chengdu, Sichuan 610041 China
| | - Lu Lu
- 0000 0004 1770 1022grid.412901.fHuaxi MR Research Center (HMRRC), Department of Radiology, West China Hospital of Sichuan University, 37 Guo Xue Xiang, Chengdu, Sichuan 610041 China
| | - Xiaoxiao Hu
- 0000 0004 1770 1022grid.412901.fHuaxi MR Research Center (HMRRC), Department of Radiology, West China Hospital of Sichuan University, 37 Guo Xue Xiang, Chengdu, Sichuan 610041 China
| | - Hailong Li
- 0000 0004 1770 1022grid.412901.fHuaxi MR Research Center (HMRRC), Department of Radiology, West China Hospital of Sichuan University, 37 Guo Xue Xiang, Chengdu, Sichuan 610041 China
| | - Yanchun Yang
- 0000 0004 1770 1022grid.412901.fMental Health Center, Department of Psychiatry, West China Hospital of Sichuan University, 37 Guo Xue Xiang, Chengdu, Sichuan 610041 China
| | - Wanjie Tang
- 0000 0004 1770 1022grid.412901.fMental Health Center, Department of Psychiatry, West China Hospital of Sichuan University, 37 Guo Xue Xiang, Chengdu, Sichuan 610041 China
| | - Qiyong Gong
- 0000 0004 1770 1022grid.412901.fHuaxi MR Research Center (HMRRC), Department of Radiology, West China Hospital of Sichuan University, 37 Guo Xue Xiang, Chengdu, Sichuan 610041 China
| | - Xiaoqi Huang
- Huaxi MR Research Center (HMRRC), Department of Radiology, West China Hospital of Sichuan University, 37 Guo Xue Xiang, Chengdu, Sichuan, 610041, China.
| |
Collapse
|
37
|
Hazari N, Narayanaswamy JC, Venkatasubramanian G. Neuroimaging findings in obsessive-compulsive disorder: A narrative review to elucidate neurobiological underpinnings. Indian J Psychiatry 2019; 61:S9-S29. [PMID: 30745673 PMCID: PMC6343409 DOI: 10.4103/psychiatry.indianjpsychiatry_525_18] [Citation(s) in RCA: 31] [Impact Index Per Article: 6.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 11/04/2022] Open
Abstract
Obsessive compulsive disorder (OCD) is a common psychiatric illness and significant research has been ongoing to understand its neurobiological basis. Neuroimaging studies right from the 1980s have revealed significant differences between OCD patients and healthy controls. Initial imaging findings showing hyperactivity in the prefrontal cortex (mainly orbitofrontal cortex), anterior cingulate cortex and caudate nucleus led to the postulation of the cortico-striato-thalamo-cortical (CSTC) model for the neurobiology of OCD. However, in the last two decades emerging evidence suggests the involvement of widespread associative networks, including regions of the parietal cortex, limbic areas (including amygdala) and cerebellum. This narrative review discusses findings from structural [Magnetic Resonance Imaging (MRI), Diffusion Tensor Imaging(DTI)], functional [(functional MRI (fMRI), Single photon emission computed tomography (SPECT), Positron emission tomography (PET), functional near-infrared spectroscopy (fNIRS)], combined structural and functional imaging studies and meta-analyses. Subsequently, we collate these findings to describe the neurobiology of OCD including CSTC circuit, limbic system, parietal cortex, cerebellum, default mode network and salience network. In future, neuroimaging may emerge as a valuable tool for personalised medicine in OCD treatment.
Collapse
Affiliation(s)
- Nandita Hazari
- Department of Psychiatry, Vidyasagar Institute of Mental Health and Neurosciences, Delhi, India
| | - Janardhanan C Narayanaswamy
- Department of Psychiatry, OCD Clinic, National Institute of Mental Health and Neurosciences, Bengaluru, Karnataka, India
| | - Ganesan Venkatasubramanian
- Department of Psychiatry, OCD Clinic, National Institute of Mental Health and Neurosciences, Bengaluru, Karnataka, India
| |
Collapse
|
38
|
Wang J, Li Y, Wang Y, Huang W. Multimodal Data and Machine Learning for Detecting Specific Biomarkers in Pediatric Epilepsy Patients With Generalized Tonic-Clonic Seizures. Front Neurol 2018; 9:1038. [PMID: 30619025 PMCID: PMC6297879 DOI: 10.3389/fneur.2018.01038] [Citation(s) in RCA: 15] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/07/2018] [Accepted: 11/19/2018] [Indexed: 01/16/2023] Open
Abstract
Previous neuroimaging studies of epilepsy with generalized tonic-clonic seizures (GTCS) focus mainly on adults. However, the neural mechanisms that underline this type of epilepsy remain unclear, especially for children. The aim of the present study was to detect the effect of epilepsy on brains of children with GTCS and to investigate whether the changes in the brain can be used to discriminate between epileptic children and healthy children at the level of the individual. To achieve this purpose, we measured gray matter (GM) volume and fractional amplitude of low-frequency fluctuation (fALFF) differences on multimodel magnetic resonance imaging in 14 children with GTCS and 30 age- and gender-matched healthy controls. The patients showed GM volume reduction and a fALFF increase in the thalamus, hippocampus, temporal and other deep nuclei. A significant decrease of fALFF was mainly found in the default mode network (DMN). In addition, epileptic duration was significantly negatively related to the GM volumes and significantly positively related to the fALFF value of right thalamus. A support vector machine (SVM) applied to the GM volume of the right thalamus correctly identified epileptic children with a statistically significant accuracy of 74.42% (P < 0.002). A SVM applied to the fALFF of the right thalamus correctly identified epileptic children with a statistically significant accuracy of 83.72% (P < 0.002). The consistent neuroimaging results indicated that the right thalamus plays an important role in reflecting the chronic damaging effect of GTCS epilepsy in children. The length of time of a child's epileptic history was correlated with greater GM volume reduction and a fALFF increase in the right thalamus. GM volumes and fALFF values in the right thalamus can identify children with GTCS from the healthy controls with high accuracy and at an individual subject level. These results are likely to be valuable in explaining the clinical problems and understanding the brain abnormalities underlying this disorder.
Collapse
Affiliation(s)
- Jianping Wang
- The Second Affiliated Hospital of Guangzhou Medical University, Guangzhou, China
| | - Yongxin Li
- Guangdong Provincial Key Laboratory of Medical Biomechanics, School of Basic Medical Sciences, Southern Medical University, Guangzhou, China
| | - Ya Wang
- Guangdong Provincial Key Laboratory of Medical Biomechanics, School of Basic Medical Sciences, Southern Medical University, Guangzhou, China
| | - Wenhua Huang
- Guangdong Provincial Key Laboratory of Medical Biomechanics, School of Basic Medical Sciences, Southern Medical University, Guangzhou, China
| |
Collapse
|
39
|
Hawco C, Voineskos AN, Radhu N, Rotenberg D, Ameis S, Backhouse FA, Semeralul M, Daskalakis ZJ. Age and gender interactions in white matter of schizophrenia and obsessive compulsive disorder compared to non-psychiatric controls: commonalities across disorders. Brain Imaging Behav 2018; 11:1836-1848. [PMID: 27915397 DOI: 10.1007/s11682-016-9657-8] [Citation(s) in RCA: 16] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/14/2022]
Abstract
Schizophrenia (SCZ) and obsessive-compulsive disorder (OCD) are psychiatric disorders with abnormalities in white matter structure. These disorders share high comorbidity and family history of OCD is a risk factor for SCZ which suggests some shared neurobiology. White matter was examined using diffusion tensor imaging in relativity large samples of SCZ (N = 48), OCD (N = 38) and non-psychiatric controls (N = 45). Fractional anisotropy (FA) was calculated and tract based spatial statistics were used to compare groups. In a whole brain analysis, SCZ and OCD both showed small FA reductions relative to controls in the corpus callosum. Both SCZ and OCD showed accelerated reductions in FA with age; specifically in the left superior longitudinal fasciculus in OCD, while the SCZ group demonstrated a more widespread pattern of FA reduction. Patient groups did not differ from each other in total FA or age effects in any regions. A general linear model using 13 a-priori regions of interest showed marginal group, group*gender, and group*age interactions. When OCD and SCZ groups were analyzed together, these marginal effects became significant (p < 0.05), suggesting commonalities exist between these patient groups. Overall, our results demonstrate a similar pattern of accelerated white matter decline with age and greater white matter deficit in females in OCD and SCZ, with overlap in the spatial pattern of deficits. There was no evidence for statistical differences in overall white matter between OCD and SCZ. Taken together, the results support the notion of shared neurobiology in SCZ and OCD.
Collapse
Affiliation(s)
- Colin Hawco
- Centre for Addiction and Mental Health, Campbell Family Mental Health Research Institute, Unit 4-1, Office 125, 1001 Queen Street West, Toronto, ON, M6J 1H4, Canada. .,Temerty Centre for Therapeutic Brain Intervention, Centre for Addiction and Mental Health, Toronto, ON, Canada.
| | - Aristotle N Voineskos
- Centre for Addiction and Mental Health, Campbell Family Mental Health Research Institute, Unit 4-1, Office 125, 1001 Queen Street West, Toronto, ON, M6J 1H4, Canada.,Department of Psychiatry, University of Toronto, Toronto, ON, Canada
| | - Natasha Radhu
- Centre for Addiction and Mental Health, Campbell Family Mental Health Research Institute, Unit 4-1, Office 125, 1001 Queen Street West, Toronto, ON, M6J 1H4, Canada.,Temerty Centre for Therapeutic Brain Intervention, Centre for Addiction and Mental Health, Toronto, ON, Canada
| | - David Rotenberg
- Centre for Addiction and Mental Health, Campbell Family Mental Health Research Institute, Unit 4-1, Office 125, 1001 Queen Street West, Toronto, ON, M6J 1H4, Canada
| | - Stephanie Ameis
- Centre for Addiction and Mental Health, Campbell Family Mental Health Research Institute, Unit 4-1, Office 125, 1001 Queen Street West, Toronto, ON, M6J 1H4, Canada.,Department of Psychiatry, University of Toronto, Toronto, ON, Canada
| | - Felicity A Backhouse
- Centre for Addiction and Mental Health, Campbell Family Mental Health Research Institute, Unit 4-1, Office 125, 1001 Queen Street West, Toronto, ON, M6J 1H4, Canada.,Temerty Centre for Therapeutic Brain Intervention, Centre for Addiction and Mental Health, Toronto, ON, Canada
| | - Mawahib Semeralul
- Temerty Centre for Therapeutic Brain Intervention, Centre for Addiction and Mental Health, Toronto, ON, Canada
| | - Zafiris J Daskalakis
- Centre for Addiction and Mental Health, Campbell Family Mental Health Research Institute, Unit 4-1, Office 125, 1001 Queen Street West, Toronto, ON, M6J 1H4, Canada.,Temerty Centre for Therapeutic Brain Intervention, Centre for Addiction and Mental Health, Toronto, ON, Canada.,Department of Psychiatry, University of Toronto, Toronto, ON, Canada
| |
Collapse
|
40
|
A common brain network among state, trait, and pathological anxiety from whole-brain functional connectivity. Neuroimage 2018; 172:506-516. [DOI: 10.1016/j.neuroimage.2018.01.080] [Citation(s) in RCA: 35] [Impact Index Per Article: 5.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/30/2017] [Revised: 01/27/2018] [Accepted: 01/30/2018] [Indexed: 01/18/2023] Open
|
41
|
Zhou C, Cheng Y, Ping L, Xu J, Shen Z, Jiang L, Shi L, Yang S, Lu Y, Xu X. Support Vector Machine Classification of Obsessive-Compulsive Disorder Based on Whole-Brain Volumetry and Diffusion Tensor Imaging. Front Psychiatry 2018; 9:524. [PMID: 30405461 PMCID: PMC6206075 DOI: 10.3389/fpsyt.2018.00524] [Citation(s) in RCA: 24] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/21/2018] [Accepted: 10/03/2018] [Indexed: 01/17/2023] Open
Abstract
Magnetic resonance imaging (MRI) methods have been used to detect cerebral anatomical distinction between obsessive-compulsive disorder (OCD) patients and healthy controls (HC). Machine learning approach allows for the possibility of discriminating patients on the individual level. However, few studies have used this automatic technique based on multiple modalities to identify potential biomarkers of OCD. High-resolution structural MRI and diffusion tensor imaging (DTI) data were acquired from 48 OCD patients and 45 well-matched HC. Gray matter volume (GMV), white matter volume (WMV), fractional anisotropy (FA), and mean diffusivity (MD) were extracted as four features were examined using support vector machine (SVM). Ten brain regions of each feature contributed most to the classification were also estimated. Using different algorithms, the classifier achieved accuracies of 72.08, 61.29, 80.65, and 77.42% for GMV, WMV, FA, and MD, respectively. The most discriminative gray matter regions that contributed to the classification were mainly distributed in the orbitofronto-striatal "affective" circuit, the dorsolateral, prefronto-striatal "executive" circuit and the cerebellum. For WMV feature and the two feature sets of DTI, the shared regions contributed the most to the discrimination mainly included the uncinate fasciculus, the cingulum in the hippocampus, corticospinal tract, as well as cerebellar peduncle. Based on whole-brain volumetry and DTI images, SVM algorithm revealed high accuracies for distinguishing OCD patients from healthy subjects at the individual level. Computer-aided method is capable of providing accurate diagnostic information and might provide a new perspective for clinical diagnosis of OCD.
Collapse
Affiliation(s)
- Cong Zhou
- Department of Psychiatry, First Affiliated Hospital of Kunming Medical University, Kunming, China.,Postgraduate College, Kunming Medical University, Kunming, China
| | - Yuqi Cheng
- Department of Psychiatry, First Affiliated Hospital of Kunming Medical University, Kunming, China
| | - Liangliang Ping
- Department of Psychiatry, First Affiliated Hospital of Kunming Medical University, Kunming, China.,Postgraduate College, Kunming Medical University, Kunming, China
| | - Jian Xu
- Department of Internal Medicine, First Affiliated Hospital of Kunming Medical University, Kunming, China
| | - Zonglin Shen
- Department of Psychiatry, First Affiliated Hospital of Kunming Medical University, Kunming, China
| | - Linling Jiang
- Department of Psychiatry, First Affiliated Hospital of Kunming Medical University, Kunming, China
| | - Li Shi
- Department of Internal Medicine, First Affiliated Hospital of Kunming Medical University, Kunming, China
| | - Shuran Yang
- Department of Psychiatry, First Affiliated Hospital of Kunming Medical University, Kunming, China
| | - Yi Lu
- Department of Medical Imaging, First Affiliated Hospital of Kunming Medical University, Kunming, China
| | - Xiufeng Xu
- Department of Psychiatry, First Affiliated Hospital of Kunming Medical University, Kunming, China
| |
Collapse
|
42
|
Mufford MS, Stein DJ, Dalvie S, Groenewold NA, Thompson PM, Jahanshad N. Neuroimaging genomics in psychiatry-a translational approach. Genome Med 2017; 9:102. [PMID: 29179742 PMCID: PMC5704437 DOI: 10.1186/s13073-017-0496-z] [Citation(s) in RCA: 38] [Impact Index Per Article: 5.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/29/2022] Open
Abstract
Neuroimaging genomics is a relatively new field focused on integrating genomic and imaging data in order to investigate the mechanisms underlying brain phenotypes and neuropsychiatric disorders. While early work in neuroimaging genomics focused on mapping the associations of candidate gene variants with neuroimaging measures in small cohorts, the lack of reproducible results inspired better-powered and unbiased large-scale approaches. Notably, genome-wide association studies (GWAS) of brain imaging in thousands of individuals around the world have led to a range of promising findings. Extensions of such approaches are now addressing epigenetics, gene–gene epistasis, and gene–environment interactions, not only in brain structure, but also in brain function. Complementary developments in systems biology might facilitate the translation of findings from basic neuroscience and neuroimaging genomics to clinical practice. Here, we review recent approaches in neuroimaging genomics—we highlight the latest discoveries, discuss advantages and limitations of current approaches, and consider directions by which the field can move forward to shed light on brain disorders.
Collapse
Affiliation(s)
- Mary S Mufford
- UCT/MRC Human Genetics Research Unit, Division of Human Genetics, Department of Pathology, Institute of Infectious Disease and Molecular Medicine, Faculty of Health Sciences, University of Cape Town, Cape Town, South Africa, 7925
| | - Dan J Stein
- MRC Unit on Risk and Resilience, Faculty of Health Sciences, University of Cape Town, Cape Town, South Africa, 7925.,Department of Psychiatry and Mental Health, Groote Schuur Hospital, Cape Town, South Africa, 7925
| | - Shareefa Dalvie
- Department of Psychiatry and Mental Health, University of Cape Town, Cape Town, South Africa, 7925
| | - Nynke A Groenewold
- Department of Psychiatry and Mental Health, Groote Schuur Hospital, Cape Town, South Africa, 7925
| | - Paul M Thompson
- Imaging Genetics Center, Mark and Mary Stevens Neuroimaging and Informatics Institute, Keck School of Medicine of the University of Southern California, Los Angeles, CA, 90292, USA
| | - Neda Jahanshad
- Imaging Genetics Center, Mark and Mary Stevens Neuroimaging and Informatics Institute, Keck School of Medicine of the University of Southern California, Los Angeles, CA, 90292, USA.
| |
Collapse
|
43
|
Trambaiolli LR, Biazoli CE, Balardin JB, Hoexter MQ, Sato JR. The relevance of feature selection methods to the classification of obsessive-compulsive disorder based on volumetric measures. J Affect Disord 2017; 222:49-56. [PMID: 28672179 DOI: 10.1016/j.jad.2017.06.061] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/15/2017] [Revised: 06/01/2017] [Accepted: 06/26/2017] [Indexed: 01/11/2023]
Abstract
BACKGROUND Magnetic resonance images (MRI) show detectable anatomical and functional differences between individuals with obsessive-compulsive disorder (OCD) and healthy subjects. Moreover, machine learning techniques have been proposed as tools to identify potential biomarkers and, ultimately, to support clinical diagnosis. However, few studies to date have investigated feature selection (FS) influences in OCD MRI-based classification. METHODS Volumes of cortical and subcortical structures, from MRI data of 38 OCD patients (split into two groups according symptoms severity) and 36 controls, were submitted to seven feature selection algorithms. FS aims to select the most relevant and less redundant features which discriminate between two classes. Then, a classification step was applied, from which the classification performances before and after different FS were compared. For the performance evaluation, leave-one-subject-out accuracies of Support Vector Machine classifiers were considered. RESULTS Using different FS algorithms, performance improvement was achieved for Controls vs. All OCD discrimination (19.08% of improvement reducing by 80% the amount of features), Controls vs. Low OCD (20.10%, 75%), Controls vs. High OCD (17.32%, 85%) and Low OCD vs. High OCD (10.53%, 75%). Furthermore, all algorithms pointed out classical cortico-striato-thalamo-cortical circuitry structures as relevant features for OCD classification. LIMITATIONS Limitations include the sample size and using only filter approaches for FS. CONCLUSIONS Our results suggest that FS positively impacts OCD classification using machine-learning techniques. Complementarily, FS algorithms were able to select biologically plausible features automatically.
Collapse
Affiliation(s)
- Lucas R Trambaiolli
- Center of Mathematics, Computing and Cognition, Universidade Federal do ABC, Rua Santa Adélia, 166, Santo André, SP 09210-170, Brazil.
| | - Claudinei E Biazoli
- Center of Mathematics, Computing and Cognition, Universidade Federal do ABC, Rua Santa Adélia, 166, Santo André, SP 09210-170, Brazil
| | - Joana B Balardin
- Center of Mathematics, Computing and Cognition, Universidade Federal do ABC, Rua Santa Adélia, 166, Santo André, SP 09210-170, Brazil
| | - Marcelo Q Hoexter
- Department and Institute of Psychiatry, University of São Paulo Medical School, Rua Dr. Ovídio Pires de Campos, 785, São Paulo 01060-970, SP, Brazil
| | - João R Sato
- Center of Mathematics, Computing and Cognition, Universidade Federal do ABC, Rua Santa Adélia, 166, Santo André, SP 09210-170, Brazil
| |
Collapse
|
44
|
A Neural Marker of Obsessive-Compulsive Disorder from Whole-Brain Functional Connectivity. Sci Rep 2017; 7:7538. [PMID: 28790433 PMCID: PMC5548868 DOI: 10.1038/s41598-017-07792-7] [Citation(s) in RCA: 49] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/13/2017] [Accepted: 07/04/2017] [Indexed: 01/06/2023] Open
Abstract
Obsessive-compulsive disorder (OCD) is a common psychiatric disorder with a lifetime prevalence of 2–3%. Recently, brain activity in the resting state is gathering attention for exploring altered functional connectivity in psychiatric disorders. Although previous resting-state functional magnetic resonance imaging studies investigated the neurobiological abnormalities of patients with OCD, there are concerns that should be addressed. One concern is the validity of the hypothesis employed. Most studies used seed-based analysis of the fronto-striatal circuit, despite the potential for abnormalities in other regions. A hypothesis-free study is a promising approach in such a case, while it requires researchers to handle a dataset with large dimensions. Another concern is the reliability of biomarkers derived from a single dataset, which may be influenced by cohort-specific features. Here, our machine learning algorithm identified an OCD biomarker that achieves high accuracy for an internal dataset (AUC = 0.81; N = 108) and demonstrates generalizability to an external dataset (AUC = 0.70; N = 28). Our biomarker was unaffected by medication status, and the functional networks contributing to the biomarker were distributed widely, including the frontoparietal and default mode networks. Our biomarker has the potential to deepen our understanding of OCD and to be applied clinically.
Collapse
|
45
|
Relationship between symptom dimensions and white matter alterations in obsessive-compulsive disorder. Acta Neuropsychiatr 2017; 29:153-163. [PMID: 27620171 DOI: 10.1017/neu.2016.45] [Citation(s) in RCA: 19] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 11/06/2022]
Abstract
OBJECTIVE To investigate the relationship between the severities of symptom dimensions in obsessive-compulsive disorder (OCD) and white matter alterations. METHODS We applied tract-based spatial statistics for diffusion tensor imaging (DTI) acquired by 3T magnetic resonance imaging. First, we compared fractional anisotropy (FA) between 20 OCD patients and 30 healthy controls (HC). Then, applying whole brain analysis, we searched the brain regions showing correlations between the severities of symptom dimensions assessed by Obsessive-Compulsive Inventory-Revised and FA in all participants. Finally, we calculated the correlations between the six symptom dimensions and multiple DTI measures [FA, axial diffusivity (AD), radial diffusivity (RD), mean diffusivity (MD)] in a region-of-interest (ROI) analysis and explored the differences between OCD patients and HC. RESULTS There were no between-group differences in FA or brain region correlations between the severities of symptom dimensions and FA in any of the participants. ROI analysis revealed negative correlations between checking severity and left inferior frontal gyrus white matter and left middle temporal gyrus white matter and a positive correlation between ordering severity and right precuneus in FA in OCD compared with HC. We also found negative correlations between ordering severity and right precuneus in RD, between obsessing severities and right supramarginal gyrus in AD and MD, and between hoarding severity and right insular gyrus in AD. CONCLUSION Our study supported the hypothesis that the severities of respective symptom dimensions are associated with different patterns of white matter alterations.
Collapse
|
46
|
Tylee DS, Kikinis Z, Quinn TP, Antshel KM, Fremont W, Tahir MA, Zhu A, Gong X, Glatt SJ, Coman IL, Shenton ME, Kates WR, Makris N. Machine-learning classification of 22q11.2 deletion syndrome: A diffusion tensor imaging study. NEUROIMAGE-CLINICAL 2017; 15:832-842. [PMID: 28761808 PMCID: PMC5522376 DOI: 10.1016/j.nicl.2017.04.029] [Citation(s) in RCA: 21] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 02/26/2017] [Revised: 03/27/2017] [Accepted: 04/04/2017] [Indexed: 11/27/2022]
Abstract
Chromosome 22q11.2 deletion syndrome (22q11.2DS) is a genetic neurodevelopmental syndrome that has been studied intensively in order to understand relationships between the genetic microdeletion, brain development, cognitive function, and the emergence of psychiatric symptoms. White matter microstructural abnormalities identified using diffusion tensor imaging methods have been reported to affect a variety of neuroanatomical tracts in 22q11.2DS. In the present study, we sought to combine two discovery-based approaches: (1) white matter query language was used to parcellate the brain's white matter into tracts connecting pairs of 34, bilateral cortical regions and (2) the diffusion imaging characteristics of the resulting tracts were analyzed using a machine-learning method called support vector machine in order to optimize the selection of a set of imaging features that maximally discriminated 22q11.2DS and comparison subjects. With this unique approach, we both confirmed previously-recognized 22q11.2DS-related abnormalities in the inferior longitudinal fasciculus (ILF), and identified, for the first time, 22q11.2DS-related anomalies in the middle longitudinal fascicle and the extreme capsule, which may have been overlooked in previous, hypothesis-guided studies. We further observed that, in participants with 22q11.2DS, ILF metrics were significantly associated with positive prodromal symptoms of psychosis.
Collapse
Key Words
- (-fp), fronto-parietal aspect
- (-to), temporo-occipital aspect
- (-tp), temporo-parietal aspect
- (22q11.2DS), 22q11.2 deletion syndrome
- (AD), axial diffusivity
- (DTI), diffusion tensor imaging
- (DWI), diffusion weighted image
- (EmC), extreme capsule
- (FA), fractional anisotropy
- (FOV), field of view
- (GDS), Gordon Diagnostic Systems
- (ILF), inferior longitudinal fasciculus
- (MdLF), middle longitudinal fascicle
- (RD), radial diffusivity
- (ROI), region of interest
- (SIPS), Structured Interview for Prodromal Syndromes
- (SRS), Social Responsiveness Scale
- (STG), superior temporal gyrus
- (SVM), support vector machine
- (UKF), Unscented Kalman Filter
- (WAIS-III), Wechsler Adult Intelligence Scale – 3rd edition
- (WMQL), white matter query language
- (dTP), dorsal temporal pole
- 22q11.2 deletion syndrome
- Callosal asymmetry
- Diffusion tensor imaging
- Extreme capsule
- Inferior longitudinal fasciculus
- Machine-learning
- Middle longitudinal fascicle
- Support vector machine
- Velocardiofacial syndrome
- White matter query language
Collapse
Affiliation(s)
- Daniel S Tylee
- Department of Neuroscience and Physiology, SUNY Upstate Medical University, Syracuse, NY, USA; Department of Psychiatry and Behavioral Sciences; SUNY Upstate Medical University, Syracuse, NY, USA
| | - Zora Kikinis
- Department of Psychiatry, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA.
| | - Thomas P Quinn
- Bioinformatics Core Research Group, Deakin University, Geelong, Victoria, Australia
| | | | - Wanda Fremont
- Department of Psychiatry and Behavioral Sciences; SUNY Upstate Medical University, Syracuse, NY, USA.
| | - Muhammad A Tahir
- Department of Psychiatry and Behavioral Sciences; SUNY Upstate Medical University, Syracuse, NY, USA
| | - Anni Zhu
- Department of Psychiatry, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
| | - Xue Gong
- Department of Psychiatry, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
| | - Stephen J Glatt
- Department of Neuroscience and Physiology, SUNY Upstate Medical University, Syracuse, NY, USA; Department of Psychiatry and Behavioral Sciences; SUNY Upstate Medical University, Syracuse, NY, USA.
| | - Ioana L Coman
- Department of Psychiatry and Behavioral Sciences; SUNY Upstate Medical University, Syracuse, NY, USA.
| | - 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; VA Boston Healthcare System, Harvard Medical School, Brockton, MA, USA.
| | - Wendy R Kates
- Department of Psychiatry and Behavioral Sciences; SUNY Upstate Medical University, Syracuse, NY, USA.
| | - Nikos Makris
- 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.
| |
Collapse
|
47
|
Tang Z, Liu Z, Li R, Yang X, Cui X, Wang S, Yu D, Li H, Dong E, Tian J. Identifying the white matter impairments among ART-naïve HIV patients: a multivariate pattern analysis of DTI data. Eur Radiol 2017; 27:4153-4162. [PMID: 28396994 DOI: 10.1007/s00330-017-4820-1] [Citation(s) in RCA: 28] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/14/2017] [Revised: 03/04/2017] [Accepted: 03/17/2017] [Indexed: 10/19/2022]
Abstract
OBJECTIVE To identify the white matter (WM) impairments of the antiretroviral therapy (ART)-naïve HIV patients by conducting a multivariate pattern analysis (MVPA) of Diffusion Tensor Imaging (DTI) data METHODS: We enrolled 33 ART-naïve HIV patients and 32 Normal controls in the current study. Firstly, the DTI metrics in whole brain WM tracts were extracted for each subject and feed into the Least Absolute Shrinkage and Selection Operators procedure (LASSO)-Logistic regression model to identify the impaired WM tracts. Then, Support Vector Machines (SVM) model was constructed based on the DTI metrics in the impaired WM tracts to make HIV-control group classification. Pearson correlations between the WM impairments and HIV clinical statics were also investigated. RESULTS Extensive HIV-related impairments were observed in the WM tracts associated with motor function, the corpus callosum (CC) and the frontal WM. With leave-one-out cross validation, accuracy of 83.08% (P=0.002) and the area under the Receiver Operating Characteristic curve of 0.9110 were obtained in the SVM classification model. The impairments of the CC were significantly correlated with the HIV clinic statics. CONCLUSION The MVPA was sensitive to detect the HIV-related WM changes. Our findings indicated that the MVPA had considerable potential in exploring the HIV-related WM impairments. KEY POINTS • WM impairments along motor pathway were detected among the ART-naïve HIV patients • Prominent HIV-related WM impairments were observed in CC and frontal WM • The impairments of CC were significantly related to the HIV clinic statics • The CC might be susceptible to immune dysfunction and HIV replication • Multivariate pattern analysis had potential for studying the HIV-related white matter impairments.
Collapse
Affiliation(s)
- Zhenchao Tang
- School of Mechanical, Electrical & Information Engineering, Shandong University, Weihai, Shandong Province, 264209, China.,CAS Key Laboratory of Molecular Imaging, Institute of Automation, Beijing, 100190, China
| | - Zhenyu Liu
- CAS Key Laboratory of Molecular Imaging, Institute of Automation, Beijing, 100190, China
| | - Ruili Li
- Department of Radiology, Beijing YouAn Hospital, Capital Medical University, Beijing, 100069, China
| | - Xin Yang
- CAS Key Laboratory of Molecular Imaging, Institute of Automation, Beijing, 100190, China
| | - Xingwei Cui
- Cooperative Innovation Center of Internet Healthcare, Zhengzhou University, Zhengzhou, China, 450052
| | - Shuo Wang
- CAS Key Laboratory of Molecular Imaging, Institute of Automation, Beijing, 100190, China
| | - Dongdong Yu
- CAS Key Laboratory of Molecular Imaging, Institute of Automation, Beijing, 100190, China
| | - Hongjun Li
- Department of Radiology, Beijing YouAn Hospital, Capital Medical University, Beijing, 100069, China.
| | - Enqing Dong
- School of Mechanical, Electrical & Information Engineering, Shandong University, Weihai, Shandong Province, 264209, China.
| | - Jie Tian
- CAS Key Laboratory of Molecular Imaging, Institute of Automation, Beijing, 100190, China. .,University of Chinese Academy of Sciences, Beijing, 100049, China.
| |
Collapse
|
48
|
Frydman I, de Salles Andrade JB, Vigne P, Fontenelle LF. Can Neuroimaging Provide Reliable Biomarkers for Obsessive-Compulsive Disorder? A Narrative Review. Curr Psychiatry Rep 2016; 18:90. [PMID: 27549605 DOI: 10.1007/s11920-016-0729-7] [Citation(s) in RCA: 23] [Impact Index Per Article: 2.9] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 12/21/2022]
Abstract
In this integrative review, we discuss findings supporting the use neuroimaging biomarkers in the diagnosis and treatment of obsessive-compulsive disorder (OCD). To do so, we have selected the most recent studies that attempted to identify the underlying pathogenic process associated with OCD and whether they provide useful information to predict clinical features, natural history or treatment responses. Studies using functional magnetic resonance (fMRI), voxel-based morphometry (VBM), diffusion tensor imaging (DTI) and proton magnetic resonance spectroscopy (1H MRS) in OCD patients are generally supportive of an expanded version of the earlier cortico-striatal-thalamus-cortical (CSTC) model of OCD. Although it is still unclear whether this information will be incorporated into the daily clinical practice (due to current conceptual approaches to mental illness), statistical techniques, such as pattern recognition methods, appear promising in identifying OCD patients and predicting their outcomes.
Collapse
Affiliation(s)
- Ilana Frydman
- Obsessive, Compulsive, and Anxiety Spectrum Research Program, Institute of Psychiatry, Federal University of Rio de Janeiro, Rio de Janeiro, Brazil
| | - Juliana B de Salles Andrade
- Obsessive, Compulsive, and Anxiety Spectrum Research Program, Institute of Psychiatry, Federal University of Rio de Janeiro, Rio de Janeiro, Brazil
| | - Paula Vigne
- Obsessive, Compulsive, and Anxiety Spectrum Research Program, Institute of Psychiatry, Federal University of Rio de Janeiro, Rio de Janeiro, Brazil
| | - Leonardo F Fontenelle
- Obsessive, Compulsive, and Anxiety Spectrum Research Program, Institute of Psychiatry, Federal University of Rio de Janeiro, Rio de Janeiro, Brazil.
- D'Or Institute for Research and Education, Rio de Janeiro, Brazil.
- Brain and Mental Health Laboratory, Monash Institute of Cognitive and Clinical Neurosciences, Monash University, Clayton, Victoria, Australia.
- , Rua Visconde de Pirajá, 547, 617, Ipanema, Rio de Janeiro, RJ, 22410-003, Brazil.
| |
Collapse
|
49
|
Berthier ML, Roé-Vellvé N, Moreno-Torres I, Falcon C, Thurnhofer-Hemsi K, Paredes-Pacheco J, Torres-Prioris MJ, De-Torres I, Alfaro F, Gutiérrez-Cardo AL, Baquero M, Ruiz-Cruces R, Dávila G. Mild Developmental Foreign Accent Syndrome and Psychiatric Comorbidity: Altered White Matter Integrity in Speech and Emotion Regulation Networks. Front Hum Neurosci 2016; 10:399. [PMID: 27555813 PMCID: PMC4977429 DOI: 10.3389/fnhum.2016.00399] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.1] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/14/2015] [Accepted: 07/26/2016] [Indexed: 11/13/2022] Open
Abstract
Foreign accent syndrome (FAS) is a speech disorder that is defined by the emergence of a peculiar manner of articulation and intonation which is perceived as foreign. In most cases of acquired FAS (AFAS) the new accent is secondary to small focal lesions involving components of the bilaterally distributed neural network for speech production. In the past few years FAS has also been described in different psychiatric conditions (conversion disorder, bipolar disorder, and schizophrenia) as well as in developmental disorders (specific language impairment, apraxia of speech). In the present study, two adult males, one with atypical phonetic production and the other one with cluttering, reported having developmental FAS (DFAS) since their adolescence. Perceptual analysis by naïve judges could not confirm the presence of foreign accent, possibly due to the mildness of the speech disorder. However, detailed linguistic analysis provided evidence of prosodic and segmental errors previously reported in AFAS cases. Cognitive testing showed reduced communication in activities of daily living and mild deficits related to psychiatric disorders. Psychiatric evaluation revealed long-lasting internalizing disorders (neuroticism, anxiety, obsessive-compulsive disorder, social phobia, depression, alexithymia, hopelessness, and apathy) in both subjects. Diffusion tensor imaging (DTI) data from each subject with DFAS were compared with data from a group of 21 age- and gender-matched healthy control subjects. Diffusion parameters (MD, AD, and RD) in predefined regions of interest showed changes of white matter microstructure in regions previously related with AFAS and psychiatric disorders. In conclusion, the present findings militate against the possibility that these two subjects have FAS of psychogenic origin. Rather, our findings provide evidence that mild DFAS occurring in the context of subtle, yet persistent, developmental speech disorders may be associated with structural brain anomalies. We suggest that the simultaneous involvement of speech and emotion regulation networks might result from disrupted neural organization during development, or compensatory or maladaptive plasticity. Future studies are required to examine whether the interplay between biological trait-like diathesis (shyness, neuroticism) and the stressful experience of living with mild DFAS lead to the development of internalizing psychiatric disorders.
Collapse
Affiliation(s)
- Marcelo L Berthier
- Cognitive Neurology and Aphasia Unit and Cathedra ARPA of Aphasia, Centro de Investigaciones Médico-Sanitarias, Instituto de Investigación Biomédica de Málaga (IBIMA), University of Malaga Malaga, Spain
| | - Núria Roé-Vellvé
- Molecular Imaging Unit, Centro de Investigaciones Médico-Sanitarias, University of Malaga Malaga, Spain
| | | | - Carles Falcon
- Barcelonabeta Brain Research Center, Pasqual Maragall Foundation Barcelona, Spain
| | - Karl Thurnhofer-Hemsi
- Molecular Imaging Unit, Centro de Investigaciones Médico-Sanitarias, University of MalagaMalaga, Spain; Department of Applied Mathematics, Superior Technical School of Engineering in Informatics, University of MalagaMalaga, Spain
| | - José Paredes-Pacheco
- Molecular Imaging Unit, Centro de Investigaciones Médico-Sanitarias, University of MalagaMalaga, Spain; Department of Applied Mathematics, Superior Technical School of Engineering in Informatics, University of MalagaMalaga, Spain
| | - María J Torres-Prioris
- Cognitive Neurology and Aphasia Unit and Cathedra ARPA of Aphasia, Centro de Investigaciones Médico-Sanitarias, Instituto de Investigación Biomédica de Málaga (IBIMA), University of MalagaMalaga, Spain; Department of Psychobiology and Methodology of Behavioural Sciences, Faculty of Psychology, University of MalagaMalaga, Spain
| | - Irene De-Torres
- Cognitive Neurology and Aphasia Unit and Cathedra ARPA of Aphasia, Centro de Investigaciones Médico-Sanitarias, Instituto de Investigación Biomédica de Málaga (IBIMA), University of MalagaMalaga, Spain; Unit of Physical Medicine and Rehabilitation, Regional University Hospital, MalagaMalaga, Spain
| | - Francisco Alfaro
- Molecular Imaging Unit, Centro de Investigaciones Médico-Sanitarias, University of Malaga Malaga, Spain
| | - Antonio L Gutiérrez-Cardo
- Molecular Imaging Unit, Centro de Investigaciones Médico-Sanitarias, University of Malaga Malaga, Spain
| | - Miquel Baquero
- Service of Neurology, Hospital Universitari i Politècnic La Fe Valencia, Spain
| | - Rafael Ruiz-Cruces
- Cognitive Neurology and Aphasia Unit and Cathedra ARPA of Aphasia, Centro de Investigaciones Médico-Sanitarias, Instituto de Investigación Biomédica de Málaga (IBIMA), University of Malaga Malaga, Spain
| | - Guadalupe Dávila
- Cognitive Neurology and Aphasia Unit and Cathedra ARPA of Aphasia, Centro de Investigaciones Médico-Sanitarias, Instituto de Investigación Biomédica de Málaga (IBIMA), University of MalagaMalaga, Spain; Department of Psychobiology and Methodology of Behavioural Sciences, Faculty of Psychology, University of MalagaMalaga, Spain
| |
Collapse
|
50
|
Smyser CD, Dosenbach NUF, Smyser TA, Snyder AZ, Rogers CE, Inder TE, Schlaggar BL, Neil JJ. Prediction of brain maturity in infants using machine-learning algorithms. Neuroimage 2016; 136:1-9. [PMID: 27179605 DOI: 10.1016/j.neuroimage.2016.05.029] [Citation(s) in RCA: 76] [Impact Index Per Article: 9.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/22/2015] [Revised: 05/05/2016] [Accepted: 05/08/2016] [Indexed: 12/24/2022] Open
Abstract
Recent resting-state functional MRI investigations have demonstrated that much of the large-scale functional network architecture supporting motor, sensory and cognitive functions in older pediatric and adult populations is present in term- and prematurely-born infants. Application of new analytical approaches can help translate the improved understanding of early functional connectivity provided through these studies into predictive models of neurodevelopmental outcome. One approach to achieving this goal is multivariate pattern analysis, a machine-learning, pattern classification approach well-suited for high-dimensional neuroimaging data. It has previously been adapted to predict brain maturity in children and adolescents using structural and resting state-functional MRI data. In this study, we evaluated resting state-functional MRI data from 50 preterm-born infants (born at 23-29weeks of gestation and without moderate-severe brain injury) scanned at term equivalent postmenstrual age compared with data from 50 term-born control infants studied within the first week of life. Using 214 regions of interest, binary support vector machines distinguished term from preterm infants with 84% accuracy (p<0.0001). Inter- and intra-hemispheric connections throughout the brain were important for group categorization, indicating that widespread changes in the brain's functional network architecture associated with preterm birth are detectable by term equivalent age. Support vector regression enabled quantitative estimation of birth gestational age in single subjects using only term equivalent resting state-functional MRI data, indicating that the present approach is sensitive to the degree of disruption of brain development associated with preterm birth (using gestational age as a surrogate for the extent of disruption). This suggests that support vector regression may provide a means for predicting neurodevelopmental outcome in individual infants.
Collapse
Affiliation(s)
- Christopher D Smyser
- Department of Neurology, Washington University School of Medicine, 660 South Euclid Avenue, Saint Louis, MO 63110-1093, USA; Department of Pediatrics, Washington University School of Medicine, 660 South Euclid Avenue, Saint Louis, MO 63110-1093, USA; Mallinckrodt Institute of Radiology, Washington University School of Medicine, 660 South Euclid Avenue, Saint Louis, MO 63110-1093, USA.
| | - Nico U F Dosenbach
- Department of Neurology, Washington University School of Medicine, 660 South Euclid Avenue, Saint Louis, MO 63110-1093, USA.
| | - Tara A Smyser
- Department of Psychiatry, Washington University School of Medicine, 660 South Euclid Avenue, Saint Louis, MO 63110-1093, USA.
| | - Abraham Z Snyder
- Department of Neurology, Washington University School of Medicine, 660 South Euclid Avenue, Saint Louis, MO 63110-1093, USA; Mallinckrodt Institute of Radiology, Washington University School of Medicine, 660 South Euclid Avenue, Saint Louis, MO 63110-1093, USA.
| | - Cynthia E Rogers
- Department of Psychiatry, Washington University School of Medicine, 660 South Euclid Avenue, Saint Louis, MO 63110-1093, USA.
| | - Terrie E Inder
- Department of Pediatric Newborn Medicine, Brigham and Women's Hospital, 75 Francis Street, Boston, MA 02115, USA.
| | - Bradley L Schlaggar
- Department of Neurology, Washington University School of Medicine, 660 South Euclid Avenue, Saint Louis, MO 63110-1093, USA; Department of Pediatrics, Washington University School of Medicine, 660 South Euclid Avenue, Saint Louis, MO 63110-1093, USA; Mallinckrodt Institute of Radiology, Washington University School of Medicine, 660 South Euclid Avenue, Saint Louis, MO 63110-1093, USA; Department of Psychiatry, Washington University School of Medicine, 660 South Euclid Avenue, Saint Louis, MO 63110-1093, USA; Department of Neurobiology, Washington University School of Medicine, 660 South Euclid Avenue, Saint Louis, MO 63110-1093, USA.
| | - Jeffrey J Neil
- Department of Neurology, Boston Children's Hospital, 333 Longwood Avenue, Boston, MA 02115, USA.
| |
Collapse
|