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Li C, Liu M, Xia J, Mei L, Yang Q, Shi F, Zhang H, Shen D. Individualized Assessment of Brain Aβ Deposition With fMRI Using Deep Learning. IEEE J Biomed Health Inform 2023; 27:5430-5438. [PMID: 37616143 DOI: 10.1109/jbhi.2023.3306460] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 08/25/2023]
Abstract
PET-based Alzheimer's disease (AD) assessment has many limitations in large-scale screening. Non-invasive techniques such as resting-state functional magnetic resonance imaging (rs-fMRI) have been proven valuable in early AD diagnosis. This study investigated feasibility of using rs-fMRI, especially functional connectivity (FC), for individualized assessment of brain amyloid-β deposition derived from PET. We designed a graph convolutional networks (GCNs) and random forest (RF) based integrated framework for using rs-fMRI-derived multi-level FC networks to predict amyloid-β PET patterns with the OASIS-3 (N = 258) and ADNI-2 (N = 291) datasets. Our method achieved satisfactory accuracy not only in Aβ-PET grade classification (for negative, intermediate, and positive grades, with accuracy in the three-class classification as 62.8% and 64.3% on two datasets, respectively), but also in prediction of whole-brain region-level Aβ-PET standard uptake value ratios (SUVRs) (with the mean square errors as 0.039 and 0.074 for two datasets, respectively). Model interpretability examination also revealed the contributive role of the limbic network. This study demonstrated high feasibility and reproducibility of using low-cost, more accessible magnetic resonance imaging (MRI) to approximate PET-based diagnosis.
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Chen X, Dai Z, Lin Y. Biotypes of major depressive disorder identified by a multiview clustering framework. J Affect Disord 2023; 329:257-272. [PMID: 36863463 DOI: 10.1016/j.jad.2023.02.118] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/24/2022] [Revised: 02/11/2023] [Accepted: 02/22/2023] [Indexed: 03/04/2023]
Abstract
BACKGROUND The advances in resting-state functional magnetic resonance imaging techniques motivate parsing heterogeneity in major depressive disorder (MDD) through neurophysiological subtypes (i.e., biotypes). Based on graph theories, researchers have observed the functional organization of the human brain as a complex system with modular structures and have found wide-spread but variable MDD-related abnormality regarding the modules. The evidence implies the possibility of identifying biotypes using high-dimensional functional connectivity (FC) data in ways that suit the potentially multifaceted biotypes taxonomy. METHODS We proposed a multiview biotype discovery framework that involves theory-driven feature subspace partition (i.e., "view") and independent subspace clustering. Six views were defined using intra- and intermodule FC regarding three MDD focal modules (i.e., the sensory-motor system, default mode network, and subcortical network). For robust biotypes, the framework was applied to a large multisite sample (805 MDD participants and 738 healthy controls). RESULTS Two biotypes were stably obtained in each view, respectively characterized by significantly increased and decreased FC compared to healthy controls. These view-specific biotypes promoted the diagnosis of MDD and showed different symptom profiles. By integrating the view-specific biotypes into biotype profiles, a broad spectrum in the neural heterogeneity of MDD and its separation from symptom-based subtypes was further revealed. LIMITATIONS The power of clinical effects is limited and the cross-sectional nature cannot predict the treatment effects of the biotypes. CONCLUSIONS Our findings not only contribute to the understanding of heterogeneity in MDD, but also provide a novel subtyping framework that could transcend current diagnostic boundaries and data modality.
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Affiliation(s)
- Xitian Chen
- Department of Psychology, Sun Yat-sen University, Guangzhou 510006, China
| | - Zhengjia Dai
- Department of Psychology, Sun Yat-sen University, Guangzhou 510006, China.
| | - Ying Lin
- Department of Psychology, Sun Yat-sen University, Guangzhou 510006, China.
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3
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Li C, Liu M, Xia J, Mei L, Yang Q, Shi F, Zhang H, Shen D. Predicting Brain Amyloid-β PET Grades with Graph Convolutional Networks Based on Functional MRI and Multi-Level Functional Connectivity. J Alzheimers Dis 2022; 86:1679-1693. [PMID: 35213377 DOI: 10.3233/jad-215497] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/15/2022]
Abstract
BACKGROUND The detection of amyloid-β (Aβ) deposition in the brain provides crucial evidence in the clinical diagnosis of Alzheimer's disease (AD). However, the current positron emission tomography (PET)-based brain Aβ examination suffers from the problems of coarse visual inspection (in many cases, with 2-class stratification) and high scanning cost. OBJECTIVE 1) To characterize the non-binary Aβ deposition levels in the AD continuum based on clustering of PET data, and 2) to explore the feasibility of predicting individual Aβ deposition grades with non-invasive functional magnetic resonance imaging (fMRI). METHODS 1) Individual whole-brain Aβ-PET images from the OASIS-3 dataset (N = 258) were grouped into three clusters (grades) with t-SNE and k-means. The demographical data as well as global and regional standard uptake value ratios (SUVRs) were compared among the three clusters with Chi-square tests or ANOVA tests. 2) From resting-state fMRI, both conventional functional connectivity (FC) and high-order FC networks were constructed and the topological architectures of the two networks were jointly learned with graph convolutional networks (GCNs) to predict the Aβ-PET grades for each individual. RESULTS We found three clearly separated clusters, indicating three Aβ-PET grades. There were significant differences in gender, age, cognitive ability, APOE type, as well as global and regional SUVRs among the three grades we found. The prediction of Aβ-PET grades with GCNs on FC for the 258 participants in the AD continuum reached a satisfactory averaged accuracy (78.8%) in the two-class classification tasks. CONCLUSION The results demonstrated the feasibility of using deep learning on a non-invasive brain functional imaging technique to approximate PET-based Aβ deposition grading.
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Affiliation(s)
- Chaolin Li
- School of Education, Guangzhou University, Guangzhou, China.,School of Biomedical Engineering, Shanghai Tech University, Shanghai, China
| | - Mianxin Liu
- School of Biomedical Engineering, Shanghai Tech University, Shanghai, China
| | - Jing Xia
- Institute of Brain-Intelligence Technology, Zhangjiang Lab, Shanghai, China
| | - Lang Mei
- School of Biomedical Engineering, Shanghai Tech University, Shanghai, China
| | - Qing Yang
- School of Biomedical Engineering, Shanghai Tech University, Shanghai, China
| | - Feng Shi
- Department of Research and Development, United Imaging Intelligence Co., Ltd., Shanghai, China
| | - Han Zhang
- School of Biomedical Engineering, Shanghai Tech University, Shanghai, China
| | - Dinggang Shen
- School of Biomedical Engineering, Shanghai Tech University, Shanghai, China.,Department of Research and Development, United Imaging Intelligence Co., Ltd., Shanghai, China
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Rahem SM, Epsi NJ, Coffman FD, Mitrofanova A. Genome-wide analysis of therapeutic response uncovers molecular pathways governing tamoxifen resistance in ER+ breast cancer. EBioMedicine 2020; 61:103047. [PMID: 33099086 PMCID: PMC7585053 DOI: 10.1016/j.ebiom.2020.103047] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/06/2020] [Revised: 09/02/2020] [Accepted: 09/18/2020] [Indexed: 01/10/2023] Open
Abstract
Background Prioritization of breast cancer patients based on the risk of resistance to tamoxifen plays a significant role in personalized therapeutic planning and improving disease course and outcomes. Methods In this work, we demonstrate that a genome-wide pathway-centric computational framework elucidates molecular pathways as markers of tamoxifen resistance in ER+ breast cancer patients. In particular, we associated activity levels of molecular pathways with a wide spectrum of response to tamoxifen, which defined markers of tamoxifen resistance in patients with ER+ breast cancer. Findings We identified five biological pathways as markers of tamoxifen failure and demonstrated their ability to predict the risk of tamoxifen resistance in two independent patient cohorts (Test cohort1: log-rank p-value = 0.02, adjusted HR = 3.11; Test cohort2: log-rank p-value = 0.01, adjusted HR = 4.24). We have shown that these pathways are not markers of aggressiveness and outperform known markers of tamoxifen response. Furthermore, for adoption into clinic, we derived a list of pathway read-out genes and their associated scoring system, which assigns a risk of tamoxifen resistance for new incoming patients. Interpretation We propose that the identified pathways and their read-out genes can be utilized to prioritize patients who would benefit from tamoxifen treatment and patients at risk of tamoxifen resistance that should be offered alternative regimens. Funding This work was supported by the Rutgers SHP Dean's research grant, Rutgers start-up funds, Libyan Ministry of Higher Education and Scientific Research, and Katrina Kehlet Graduate Award from The NJ Chapter of the Healthcare Information Management Systems Society.
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Affiliation(s)
- Sarra M Rahem
- Department of Biomedical and Health Informatics, Rutgers School of Health Professions, Rutgers Biomedical and Health Sciences, USA
| | - Nusrat J Epsi
- Department of Biomedical and Health Informatics, Rutgers School of Health Professions, Rutgers Biomedical and Health Sciences, USA
| | - Frederick D Coffman
- Department of Biomedical and Health Informatics, Rutgers School of Health Professions, Rutgers Biomedical and Health Sciences, USA; Department of Physician Assistant Studies and Practice, USA; Department of Pathology & Laboratory Medicine, New Jersey Medical School, Newark, New Jersey 07107, USA
| | - Antonina Mitrofanova
- Department of Biomedical and Health Informatics, Rutgers School of Health Professions, Rutgers Biomedical and Health Sciences, USA; Rutgers Cancer Institute of New Jersey, Rutgers, The State University of New Jersey, New Brunswick, New Jersey 08901, USA.
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5
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Ó Marcaigh F, Kelly DJ, Analuddin K, Karya A, Lawless N, Marples NM. Cryptic sexual dimorphism reveals differing selection pressures on continental islands. Biotropica 2020. [DOI: 10.1111/btp.12852] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/16/2023]
Affiliation(s)
- Fionn Ó Marcaigh
- Department of Zoology School of Natural Sciences Trinity College Dublin Dublin Ireland
| | - David J. Kelly
- Department of Zoology School of Natural Sciences Trinity College Dublin Dublin Ireland
| | - Kangkuso Analuddin
- Department of Biology and Biotechnology Universitas Halu Oleo Kendari Indonesia
| | - Adi Karya
- Department of Biology and Biotechnology Universitas Halu Oleo Kendari Indonesia
| | - Naomi Lawless
- Department of Zoology School of Natural Sciences Trinity College Dublin Dublin Ireland
| | - Nicola M. Marples
- Department of Zoology School of Natural Sciences Trinity College Dublin Dublin Ireland
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van Veen JE, Kammel LG, Bunda PC, Shum M, Reid MS, Massa MG, Arneson D, Park JW, Zhang Z, Joseph AM, Hrncir H, Liesa M, Arnold AP, Yang X, Correa SM. Hypothalamic estrogen receptor alpha establishes a sexually dimorphic regulatory node of energy expenditure. Nat Metab 2020; 2:351-363. [PMID: 32377634 PMCID: PMC7202561 DOI: 10.1038/s42255-020-0189-6] [Citation(s) in RCA: 48] [Impact Index Per Article: 12.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/13/2019] [Accepted: 03/12/2020] [Indexed: 12/26/2022]
Abstract
Estrogen receptor a (ERa) signaling in the ventromedial hypothalamus (VMH) contributes to energy homeostasis by modulating physical activity and thermogenesis. However, the precise neuronal populations involved remain undefined. Here, we describe six neuronal populations in the mouse VMH by using single-cell RNA transcriptomics and in situ hybridization. ERa is enriched in populations showing sex biased expression of reprimo (Rprm), tachykinin 1 (Tac1), and prodynorphin (Pdyn). Female biased expression of Tac1 and Rprm is patterned by ERa-dependent repression during male development, whereas male biased expression of Pdyn is maintained by circulating testicular hormone in adulthood. Chemogenetic activation of ERa positive VMH neurons stimulates heat generation and movement in both sexes. However, silencing Rprm gene function increases core temperature selectively in females and ectopic Rprm expression in males is associated with reduced core temperature. Together these findings reveal a role for Rprm in temperature regulation and ERa in the masculinization of neuron populations that underlie energy expenditure.
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Affiliation(s)
- J Edward van Veen
- Department of Integrative Biology and Physiology, University of California, Los Angeles, CA, USA
- Laboratory of Neuroendocrinology of the Brain Research Institute, University of California, Los Angeles, CA, USA
- authors contributed equally
| | - Laura G Kammel
- Department of Integrative Biology and Physiology, University of California, Los Angeles, CA, USA
- Laboratory of Neuroendocrinology of the Brain Research Institute, University of California, Los Angeles, CA, USA
- Molecular, Cellular, and Integrative Physiology Graduate Program, University of California, Los Angeles, CA, USA
- authors contributed equally
| | - Patricia C Bunda
- Department of Integrative Biology and Physiology, University of California, Los Angeles, CA, USA
| | - Michael Shum
- Division of Endocrinology, Department of Medicine, and Department of Molecular and Medical Pharmacology, David Geffen School of Medicine, University of California, Los Angeles, CA, USA
- Molecular Biology Institute, University of California, Los Angeles, CA, USA
| | - Michelle S Reid
- Department of Integrative Biology and Physiology, University of California, Los Angeles, CA, USA
| | - Megan G Massa
- Department of Integrative Biology and Physiology, University of California, Los Angeles, CA, USA
- Laboratory of Neuroendocrinology of the Brain Research Institute, University of California, Los Angeles, CA, USA
- Neuroscience Interdepartmental Doctoral Program, University of California, Los Angeles, CA, USA
| | - Douglas Arneson
- Department of Integrative Biology and Physiology, University of California, Los Angeles, CA, USA
| | - Jae W Park
- Department of Integrative Biology and Physiology, University of California, Los Angeles, CA, USA
| | - Zhi Zhang
- Department of Integrative Biology and Physiology, University of California, Los Angeles, CA, USA
- Laboratory of Neuroendocrinology of the Brain Research Institute, University of California, Los Angeles, CA, USA
| | - Alexia M Joseph
- Department of Integrative Biology and Physiology, University of California, Los Angeles, CA, USA
| | - Haley Hrncir
- Department of Integrative Biology and Physiology, University of California, Los Angeles, CA, USA
| | - Marc Liesa
- Division of Endocrinology, Department of Medicine, and Department of Molecular and Medical Pharmacology, David Geffen School of Medicine, University of California, Los Angeles, CA, USA
- Molecular Biology Institute, University of California, Los Angeles, CA, USA
| | - Arthur P Arnold
- Department of Integrative Biology and Physiology, University of California, Los Angeles, CA, USA
- Laboratory of Neuroendocrinology of the Brain Research Institute, University of California, Los Angeles, CA, USA
| | - Xia Yang
- Department of Integrative Biology and Physiology, University of California, Los Angeles, CA, USA
| | - Stephanie M Correa
- Department of Integrative Biology and Physiology, University of California, Los Angeles, CA, USA
- Laboratory of Neuroendocrinology of the Brain Research Institute, University of California, Los Angeles, CA, USA
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7
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Hattingh M, Matthee M, Smuts H, Pappas I, Dwivedi YK, Mäntymäki M. Requirements of Data Visualisation Tools to Analyse Big Data: A Structured Literature Review. LECTURE NOTES IN COMPUTER SCIENCE 2020. [PMCID: PMC7134219 DOI: 10.1007/978-3-030-44999-5_39] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 12/02/2022]
Abstract
The continual growth of big data necessitates efficient ways of analysing these large datasets. Data visualisation and visual analytics has been identified as a key tool in big data analysis because they draw on the human visual and cognitive capabilities to analyse data quickly, intuitively and interactively. However, current visualisation tools and visual analytical systems fall short of providing a seamless user experience and several improvements could be made to current commercially available visualisation tools. By conducting a systematic literature review, requirements of visualisation tools were identified and categorised into six groups: dimensionality reduction, data reduction, scalability and readability, interactivity, fast retrieval of results, and user assistance. The most common themes found in the literature were dimensionality reduction and interactive data exploration.
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Mothi SS, Sudarshan M, Tandon N, Tamminga C, Pearlson G, Sweeney J, Clementz B, Keshavan MS. Machine learning improved classification of psychoses using clinical and biological stratification: Update from the bipolar-schizophrenia network for intermediate phenotypes (B-SNIP). Schizophr Res 2019; 214:60-69. [PMID: 29807804 DOI: 10.1016/j.schres.2018.04.037] [Citation(s) in RCA: 18] [Impact Index Per Article: 3.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/25/2017] [Revised: 04/25/2018] [Accepted: 04/27/2018] [Indexed: 12/22/2022]
Affiliation(s)
- Suraj Sarvode Mothi
- Department of Psychiatry, Beth Israel Deaconess Medical Center and Harvard Medical School, 75 Fenwood Rd, Boston, MA 02115, USA; Department of Psychiatry, Massachusetts General Hospital Boston, MA 02114, USA.
| | | | - Neeraj Tandon
- Department of Psychiatry, Beth Israel Deaconess Medical Center and Harvard Medical School, 75 Fenwood Rd, Boston, MA 02115, USA; Baylor College of Medicine, Houston, TX, USA
| | | | - Godfrey Pearlson
- Olin Neuropsychiatry Research Center, Hartford, CT, USA; Department of Psychiatry and Neurobiology, Yale University, New Haven, CT, USA
| | - John Sweeney
- Department of Psychiatry, University of Cincinnati, Cincinnati, OH, USA
| | - Brett Clementz
- Department of Psychiatry, University of Cincinnati, Cincinnati, OH, USA; Department of Psychology, Bio-Imaging Research Center, University of Georgia, Athens, GA, USA
| | - Matcheri S Keshavan
- Department of Psychiatry, Beth Israel Deaconess Medical Center and Harvard Medical School, 75 Fenwood Rd, Boston, MA 02115, USA.
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Kalasekar SM, Kotiyal S, Conley C, Phan C, Young A, Evason KJ. Heterogeneous beta-catenin activation is sufficient to cause hepatocellular carcinoma in zebrafish. Biol Open 2019; 8:bio047829. [PMID: 31575545 PMCID: PMC6826293 DOI: 10.1242/bio.047829] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/10/2019] [Accepted: 09/24/2019] [Indexed: 12/19/2022] Open
Abstract
Up to 41% of hepatocellular carcinomas (HCCs) result from activating mutations in the CTNNB1 gene encoding β-catenin. HCC-associated CTNNB1 mutations stabilize the β-catenin protein, leading to nuclear and/or cytoplasmic localization of β-catenin and downstream activation of Wnt target genes. In patient HCC samples, β-catenin nuclear and cytoplasmic localization are typically patchy, even among HCC with highly active CTNNB1 mutations. The functional and clinical relevance of this heterogeneity in β-catenin activation are not well understood. To define mechanisms of β-catenin-driven HCC initiation, we generated a Cre-lox system that enabled switching on activated β-catenin in (1) a small number of hepatocytes in early development; or (2) the majority of hepatocytes in later development or adulthood. We discovered that switching on activated β-catenin in a subset of larval hepatocytes was sufficient to drive HCC initiation. To determine the role of Wnt/β-catenin signaling heterogeneity later in hepatocarcinogenesis, we performed RNA-seq analysis of zebrafish β-catenin-driven HCC. At the single-cell level, 2.9% to 15.2% of hepatocytes from zebrafish β-catenin-driven HCC expressed two or more of the Wnt target genes axin2, mtor, glula, myca and wif1, indicating focal activation of Wnt signaling in established tumors. Thus, heterogeneous β-catenin activation drives HCC initiation and persists throughout hepatocarcinogenesis.
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Affiliation(s)
- Sharanya M Kalasekar
- Department of Oncological Sciences, Huntsman Cancer Institute, University of Utah, Salt Lake City, UT 84112, USA
| | - Srishti Kotiyal
- Department of Oncological Sciences, Huntsman Cancer Institute, University of Utah, Salt Lake City, UT 84112, USA
| | - Christopher Conley
- Department of Oncological Sciences, Huntsman Cancer Institute, University of Utah, Salt Lake City, UT 84112, USA
| | - Cindy Phan
- Department of Oncological Sciences, Huntsman Cancer Institute, University of Utah, Salt Lake City, UT 84112, USA
| | - Annika Young
- Department of Oncological Sciences, Huntsman Cancer Institute, University of Utah, Salt Lake City, UT 84112, USA
| | - Kimberley J Evason
- Department of Oncological Sciences, Huntsman Cancer Institute, University of Utah, Salt Lake City, UT 84112, USA
- Department of Pathology, University of Utah, Salt Lake City, UT 84112, USA
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10
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Tracking the Brain State Transition Process of Dynamic Function Connectivity Based on Resting State fMRI. COMPUTATIONAL INTELLIGENCE AND NEUROSCIENCE 2019; 2019:9027803. [PMID: 31687008 PMCID: PMC6800976 DOI: 10.1155/2019/9027803] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 05/22/2019] [Revised: 08/19/2019] [Accepted: 09/05/2019] [Indexed: 11/17/2022]
Abstract
BOLD-fMRI technology provides a good foundation for the research of human brain dynamic functional connectivity and brain state analysis. However, due to the complexity of brain function connectivity and the high dimensionality expression of brain dynamic attributions, more research studies are focusing on tracking the time-varying characteristics through the transition between different brain states. The transition process is considered to occur instantaneously at some special time point in the above research studies, whereas our work found the brain state transition may be completed in a time section gradually rather than instantaneously. In this paper, a brain state conversion rate model is constructed to observe the procedure of brain state transition trend at each time point, and the state change can be observed by the values of conversion rate. According to the results, the transition of status always lasts for a few time points, and a brain state network model with both steady state and transition state is presented. Network topological overlap coefficient is built to analyze the features of time-varying networks. With this method, some common regular patterns of time-varying characteristics can be observed strongly in healthy children but not in the autism children. This distinct can help us to distinguish children with autism from healthy children.
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11
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Squarcina L, Dagnew TM, Rivolta MW, Bellani M, Sassi R, Brambilla P. Automated cortical thickness and skewness feature selection in bipolar disorder using a semi-supervised learning method. J Affect Disord 2019; 256:416-423. [PMID: 31229930 DOI: 10.1016/j.jad.2019.06.019] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/30/2019] [Revised: 03/26/2019] [Accepted: 06/07/2019] [Indexed: 01/10/2023]
Abstract
BACKGROUND Bipolar disorder (BD) broadly affects brain structure, in particular areas involved in emotion processing and cognition. In the last years, the psychiatric field's interest in machine learning approaches has been steadily growing, thanks to the potentiality of automatically discriminating patients from healthy controls. METHODS In this work, we employed cortical thickness of 58 regions of interest obtained from magnetic resonance imaging scans of 41 BD patients and 34 healthy controls, to automatically identify the regions which are mostly involved with the disease. We used a semi-supervised method, addressing the criticisms on supervised methods, related to the fact that the diagnosis is not unaffected by uncertainty. RESULTS Our results confirm findings in previous studies, with a classification accuracy of about 75% when mean thickness and skewness of up to five regions are considered. We obtained that the parietal lobe and some areas in the temporal sulcus were the regions which were the most involved with BD. LIMITATIONS The major limitation of our work is the limited size or our dataset, but in line with other recent machine learning works in the field. Moreover, we considered chronic patients, whose brain characteristics may thus be affected. CONCLUSIONS The automatic selection of the brain regions most involved in BD may be of great importance when dealing with the pathogenesis of the disorder. Our method selected regions which are known to be involved with BD, indicating that damage to the identified areas can be considered as a marker of disease.
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Affiliation(s)
- L Squarcina
- Department of Neurosciences and Mental Health, Fondazione IRCCS Ca' Granda Ospedale Maggiore Policlinico, University of Milan, Milan, Italy.
| | - T M Dagnew
- Department of Computer Science, University of Milan, Milan, Italy.
| | - M W Rivolta
- Department of Computer Science, University of Milan, Milan, Italy
| | - M Bellani
- Department of Neurosciences, Biomedicine and Movement Sciences, Section of Psychiatry, University of Verona, Italy
| | - R Sassi
- Department of Computer Science, University of Milan, Milan, Italy
| | - P Brambilla
- Department of Neurosciences and Mental Health, Fondazione IRCCS Ca' Granda Ospedale Maggiore Policlinico, University of Milan, Milan, Italy; Department of Pathophysiology and Transplantation, University of Milan, Milan, Italy
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Tulay EE, Metin B, Tarhan N, Arıkan MK. Multimodal Neuroimaging: Basic Concepts and Classification of Neuropsychiatric Diseases. Clin EEG Neurosci 2019; 50:20-33. [PMID: 29925268 DOI: 10.1177/1550059418782093] [Citation(s) in RCA: 36] [Impact Index Per Article: 7.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 01/08/2023]
Abstract
Neuroimaging techniques are widely used in neuroscience to visualize neural activity, to improve our understanding of brain mechanisms, and to identify biomarkers-especially for psychiatric diseases; however, each neuroimaging technique has several limitations. These limitations led to the development of multimodal neuroimaging (MN), which combines data obtained from multiple neuroimaging techniques, such as electroencephalography, functional magnetic resonance imaging, and yields more detailed information about brain dynamics. There are several types of MN, including visual inspection, data integration, and data fusion. This literature review aimed to provide a brief summary and basic information about MN techniques (data fusion approaches in particular) and classification approaches. Data fusion approaches are generally categorized as asymmetric and symmetric. The present review focused exclusively on studies based on symmetric data fusion methods (data-driven methods), such as independent component analysis and principal component analysis. Machine learning techniques have recently been introduced for use in identifying diseases and biomarkers of disease. The machine learning technique most widely used by neuroscientists is classification-especially support vector machine classification. Several studies differentiated patients with psychiatric diseases and healthy controls with using combined datasets. The common conclusion among these studies is that the prediction of diseases increases when combining data via MN techniques; however, there remain a few challenges associated with MN, such as sample size. Perhaps in the future N-way fusion can be used to combine multiple neuroimaging techniques or nonimaging predictors (eg, cognitive ability) to overcome the limitations of MN.
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Affiliation(s)
| | | | - Nevzat Tarhan
- 1 Uskudar University, Istanbul, Turkey.,2 NPIstanbul Hospital, Istanbul, Turkey
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Zaharchuk G, Gong E, Wintermark M, Rubin D, Langlotz CP. Deep Learning in Neuroradiology. AJNR Am J Neuroradiol 2018; 39:1776-1784. [PMID: 29419402 PMCID: PMC7410723 DOI: 10.3174/ajnr.a5543] [Citation(s) in RCA: 170] [Impact Index Per Article: 28.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/22/2022]
Abstract
Deep learning is a form of machine learning using a convolutional neural network architecture that shows tremendous promise for imaging applications. It is increasingly being adapted from its original demonstration in computer vision applications to medical imaging. Because of the high volume and wealth of multimodal imaging information acquired in typical studies, neuroradiology is poised to be an early adopter of deep learning. Compelling deep learning research applications have been demonstrated, and their use is likely to grow rapidly. This review article describes the reasons, outlines the basic methods used to train and test deep learning models, and presents a brief overview of current and potential clinical applications with an emphasis on how they are likely to change future neuroradiology practice. Facility with these methods among neuroimaging researchers and clinicians will be important to channel and harness the vast potential of this new method.
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Affiliation(s)
- G Zaharchuk
- From the Departments of Radiology (G.Z., M.W., D.R., C.P.L.)
| | - E Gong
- Electrical Engineering (E.G.), Stanford University and Stanford University Medical Center, Stanford, California
| | - M Wintermark
- From the Departments of Radiology (G.Z., M.W., D.R., C.P.L.)
| | - D Rubin
- From the Departments of Radiology (G.Z., M.W., D.R., C.P.L.)
| | - C P Langlotz
- From the Departments of Radiology (G.Z., M.W., D.R., C.P.L.)
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Dwyer DB, Cabral C, Kambeitz-Ilankovic L, Sanfelici R, Kambeitz J, Calhoun V, Falkai P, Pantelis C, Meisenzahl E, Koutsouleris N. Brain Subtyping Enhances The Neuroanatomical Discrimination of Schizophrenia. Schizophr Bull 2018; 44. [PMID: 29529270 PMCID: PMC6101481 DOI: 10.1093/schbul/sby008] [Citation(s) in RCA: 63] [Impact Index Per Article: 10.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 11/12/2022]
Abstract
Identifying distinctive subtypes of schizophrenia could ultimately enhance diagnostic and prognostic accuracy. We aimed to uncover neuroanatomical subtypes of chronic schizophrenia patients to test whether stratification can enhance computer-aided discrimination of patients from control subjects. Unsupervised, data-driven clustering of structural MRI (sMRI) data was used to identify 2 subtypes of schizophrenia patients drawn from a US-based open science repository (n = 71) and we quantified classification improvements compared to controls (n = 74) using supervised machine learning. We externally validated the unsupervised and supervised learning models in a heterogeneous German validation sample (n = 316), and characterized symptom, cognition, and longitudinal symptom change signatures. Stratification improved classification accuracies from 68.5% to 73% (subgroup 1) and 78.8% (subgroup 2), respectively. Increased accuracy was also found when models were externally validated, and an average gain of 9% was found in supplementary analyses. The first subgroup was associated with cortical and subcortical volume reductions coupled with substantially longer illness duration, whereas the second subgroup was mainly characterized by cortical reductions, reduced illness duration, and comparatively less negative symptoms. Individuals within each subgroup could be identified using just 10 clinical questions at an accuracy of 81.2%, and differential cognitive and symptom course signatures were suggested in multivariate analyses. Our findings suggest that sMRI-based subtyping enhances the neuroanatomical discrimination of schizophrenia by identifying generalizable brain patterns that align with a clinical staging model of the disorder. These findings could be used to improve illness stratification for biomarker-based computer-aided diagnoses.
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Affiliation(s)
- Dominic B Dwyer
- Department of Psychiatry and Psychotherapy, Ludwig-Maximilian University, Munich, Germany,To whom correspondence should be addressed; Section for Neurodiagnostic Applications, Department of Psychiatry, Ludwig Maximilian University, Nussbaumstrasse 7, 80336, Munich, Bavaria, Germany; tel: +49 (0)89 4400 55757, fax: +49 (0)89 4400 55776, e-mail:
| | - Carlos Cabral
- Department of Psychiatry and Psychotherapy, Ludwig-Maximilian University, Munich, Germany
| | | | - Rachele Sanfelici
- Department of Psychiatry and Psychotherapy, Ludwig-Maximilian University, Munich, Germany
| | - Joseph Kambeitz
- Department of Psychiatry and Psychotherapy, Ludwig-Maximilian University, Munich, Germany
| | - Vince Calhoun
- Mind Research Network and Lovelace Biomedical and Environmental Research Network, Albuquerque, NM,Department of Electrical and Computer Engineering, The University of New Mexico, Albuquerque, New Mexico
| | - Peter Falkai
- Department of Psychiatry and Psychotherapy, Ludwig-Maximilian University, Munich, Germany
| | - Christos Pantelis
- Melbourne Neuropsychiatry Centre, Department of Psychiatry, The University of Melbourne and Melbourne Health, Carlton South, VIC, Australia,Centre for Neural Engineering, Department of Electrical and Electronic Engineering, University of Melbourne, Carlton South, VIC, Australia
| | - Eva Meisenzahl
- Department of Psychiatry and Psychotherapy, Ludwig-Maximilian University, Munich, Germany
| | - Nikolaos Koutsouleris
- Department of Psychiatry and Psychotherapy, Ludwig-Maximilian University, Munich, Germany
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15
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Bridwell DA, Cavanagh JF, Collins AGE, Nunez MD, Srinivasan R, Stober S, Calhoun VD. Moving Beyond ERP Components: A Selective Review of Approaches to Integrate EEG and Behavior. Front Hum Neurosci 2018; 12:106. [PMID: 29632480 PMCID: PMC5879117 DOI: 10.3389/fnhum.2018.00106] [Citation(s) in RCA: 47] [Impact Index Per Article: 7.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/05/2018] [Accepted: 03/06/2018] [Indexed: 11/17/2022] Open
Abstract
Relationships between neuroimaging measures and behavior provide important clues about brain function and cognition in healthy and clinical populations. While electroencephalography (EEG) provides a portable, low cost measure of brain dynamics, it has been somewhat underrepresented in the emerging field of model-based inference. We seek to address this gap in this article by highlighting the utility of linking EEG and behavior, with an emphasis on approaches for EEG analysis that move beyond focusing on peaks or “components” derived from averaging EEG responses across trials and subjects (generating the event-related potential, ERP). First, we review methods for deriving features from EEG in order to enhance the signal within single-trials. These methods include filtering based on user-defined features (i.e., frequency decomposition, time-frequency decomposition), filtering based on data-driven properties (i.e., blind source separation, BSS), and generating more abstract representations of data (e.g., using deep learning). We then review cognitive models which extract latent variables from experimental tasks, including the drift diffusion model (DDM) and reinforcement learning (RL) approaches. Next, we discuss ways to access associations among these measures, including statistical models, data-driven joint models and cognitive joint modeling using hierarchical Bayesian models (HBMs). We think that these methodological tools are likely to contribute to theoretical advancements, and will help inform our understandings of brain dynamics that contribute to moment-to-moment cognitive function.
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Affiliation(s)
| | - James F Cavanagh
- Department of Psychology, University of New Mexico, Albuquerque, NM, United States
| | - Anne G E Collins
- Department of Psychology, University of California, Berkeley, Berkeley, CA, United States.,Helen Wills Neuroscience Institute, University of California, Berkeley, Berkeley, CA, United States
| | - Michael D Nunez
- Department of Cognitive Sciences, University of California, Irvine, Irvine, CA, United States.,Department of Biomedical Engineering, University of California, Irvine, Irvine, CA, United States
| | - Ramesh Srinivasan
- Department of Cognitive Sciences, University of California, Irvine, Irvine, CA, United States.,Department of Biomedical Engineering, University of California, Irvine, Irvine, CA, United States
| | - Sebastian Stober
- Research Focus Cognitive Sciences, University of Potsdam, Potsdam, Germany
| | - Vince D Calhoun
- The Mind Research Network, Albuquerque, NM, United States.,Department of ECE, University of New Mexico, Albuquerque, NM, United States
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16
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Narula S, Shameer K, Salem Omar AM, Dudley JT, Sengupta PP. Machine-Learning Algorithms to Automate Morphological and Functional Assessments in 2D Echocardiography. J Am Coll Cardiol 2017; 68:2287-2295. [PMID: 27884247 DOI: 10.1016/j.jacc.2016.08.062] [Citation(s) in RCA: 223] [Impact Index Per Article: 31.9] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/12/2016] [Revised: 07/27/2016] [Accepted: 08/17/2016] [Indexed: 11/25/2022]
Abstract
BACKGROUND Machine-learning models may aid cardiac phenotypic recognition by using features of cardiac tissue deformation. OBJECTIVES This study investigated the diagnostic value of a machine-learning framework that incorporates speckle-tracking echocardiographic data for automated discrimination of hypertrophic cardiomyopathy (HCM) from physiological hypertrophy seen in athletes (ATH). METHODS Expert-annotated speckle-tracking echocardiographic datasets obtained from 77 ATH and 62 HCM patients were used for developing an automated system. An ensemble machine-learning model with 3 different machine-learning algorithms (support vector machines, random forests, and artificial neural networks) was developed and a majority voting method was used for conclusive predictions with further K-fold cross-validation. RESULTS Feature selection using an information gain (IG) algorithm revealed that volume was the best predictor for differentiating between HCM ands. ATH (IG = 0.24) followed by mid-left ventricular segmental (IG = 0.134) and average longitudinal strain (IG = 0.131). The ensemble machine-learning model showed increased sensitivity and specificity compared with early-to-late diastolic transmitral velocity ratio (p < 0.01), average early diastolic tissue velocity (e') (p < 0.01), and strain (p = 0.04). Because ATH were younger, adjusted analysis was undertaken in younger HCM patients and compared with ATH with left ventricular wall thickness >13 mm. In this subgroup analysis, the automated model continued to show equal sensitivity, but increased specificity relative to early-to-late diastolic transmitral velocity ratio, e', and strain. CONCLUSIONS Our results suggested that machine-learning algorithms can assist in the discrimination of physiological versus pathological patterns of hypertrophic remodeling. This effort represents a step toward the development of a real-time, machine-learning-based system for automated interpretation of echocardiographic images, which may help novice readers with limited experience.
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Affiliation(s)
- Sukrit Narula
- Zena and Michael A. Weiner Cardiovascular Institute, Icahn School of Medicine at Mount Sinai, New York, New York
| | - Khader Shameer
- Institute of Next Generation Healthcare, Department of Genetics and Genomic Sciences, Mount Sinai Health System, New York, New York
| | - Alaa Mabrouk Salem Omar
- Zena and Michael A. Weiner Cardiovascular Institute, Icahn School of Medicine at Mount Sinai, New York, New York; Department of Internal Medicine, Medical Division, National Research Center, Cairo, Egypt
| | - Joel T Dudley
- Institute of Next Generation Healthcare, Department of Genetics and Genomic Sciences, Mount Sinai Health System, New York, New York
| | - Partho P Sengupta
- Zena and Michael A. Weiner Cardiovascular Institute, Icahn School of Medicine at Mount Sinai, New York, New York.
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17
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Wu MJ, Mwangi B, Bauer IE, Passos IC, Sanches M, Zunta-Soares GB, Meyer TD, Hasan KM, Soares JC. Identification and individualized prediction of clinical phenotypes in bipolar disorders using neurocognitive data, neuroimaging scans and machine learning. Neuroimage 2017; 145:254-264. [PMID: 26883067 PMCID: PMC4983269 DOI: 10.1016/j.neuroimage.2016.02.016] [Citation(s) in RCA: 81] [Impact Index Per Article: 11.6] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/15/2015] [Revised: 01/14/2016] [Accepted: 02/08/2016] [Indexed: 12/28/2022] Open
Abstract
Diagnosis, clinical management and research of psychiatric disorders remain subjective - largely guided by historically developed categories which may not effectively capture underlying pathophysiological mechanisms of dysfunction. Here, we report a novel approach of identifying and validating distinct and biologically meaningful clinical phenotypes of bipolar disorders using both unsupervised and supervised machine learning techniques. First, neurocognitive data were analyzed using an unsupervised machine learning approach and two distinct clinical phenotypes identified namely; phenotype I and phenotype II. Second, diffusion weighted imaging scans were pre-processed using the tract-based spatial statistics (TBSS) method and 'skeletonized' white matter fractional anisotropy (FA) and mean diffusivity (MD) maps extracted. The 'skeletonized' white matter FA and MD maps were entered into the Elastic Net machine learning algorithm to distinguish individual subjects' phenotypic labels (e.g. phenotype I vs. phenotype II). This calculation was performed to ascertain whether the identified clinical phenotypes were biologically distinct. Original neurocognitive measurements distinguished individual subjects' phenotypic labels with 94% accuracy (sensitivity=92%, specificity=97%). TBSS derived FA and MD measurements predicted individual subjects' phenotypic labels with 76% and 65% accuracy respectively. In addition, individual subjects belonging to phenotypes I and II were distinguished from healthy controls with 57% and 92% accuracy respectively. Neurocognitive task variables identified as most relevant in distinguishing phenotypic labels included; Affective Go/No-Go (AGN), Cambridge Gambling Task (CGT) coupled with inferior fronto-occipital fasciculus and callosal white matter pathways. These results suggest that there may exist two biologically distinct clinical phenotypes in bipolar disorders which can be identified from healthy controls with high accuracy and at an individual subject level. We suggest a strong clinical utility of the proposed approach in defining and validating biologically meaningful and less heterogeneous clinical sub-phenotypes of major psychiatric disorders.
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Affiliation(s)
- Mon-Ju Wu
- UT Center of Excellence on Mood Disorders, Department of Psychiatry and Behavioral Sciences, UT Houston Medical School, Houston, TX, USA
| | - Benson Mwangi
- UT Center of Excellence on Mood Disorders, Department of Psychiatry and Behavioral Sciences, UT Houston Medical School, Houston, TX, USA.
| | - Isabelle E Bauer
- UT Center of Excellence on Mood Disorders, Department of Psychiatry and Behavioral Sciences, UT Houston Medical School, Houston, TX, USA
| | - Ives C Passos
- UT Center of Excellence on Mood Disorders, Department of Psychiatry and Behavioral Sciences, UT Houston Medical School, Houston, TX, USA
| | - Marsal Sanches
- UT Center of Excellence on Mood Disorders, Department of Psychiatry and Behavioral Sciences, UT Houston Medical School, Houston, TX, USA
| | - Giovana B Zunta-Soares
- UT Center of Excellence on Mood Disorders, Department of Psychiatry and Behavioral Sciences, UT Houston Medical School, Houston, TX, USA
| | - Thomas D Meyer
- UT Center of Excellence on Mood Disorders, Department of Psychiatry and Behavioral Sciences, UT Houston Medical School, Houston, TX, USA
| | - Khader M Hasan
- Department of Diagnostic & Interventional Imaging, The University of Texas Health Science Center at Houston, Houston, TX, USA
| | - Jair C Soares
- UT Center of Excellence on Mood Disorders, Department of Psychiatry and Behavioral Sciences, UT Houston Medical School, Houston, TX, USA
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18
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Marquand AF, Wolfers T, Mennes M, Buitelaar J, Beckmann CF. Beyond Lumping and Splitting: A Review of Computational Approaches for Stratifying Psychiatric Disorders. BIOLOGICAL PSYCHIATRY. COGNITIVE NEUROSCIENCE AND NEUROIMAGING 2016; 1:433-447. [PMID: 27642641 PMCID: PMC5013873 DOI: 10.1016/j.bpsc.2016.04.002] [Citation(s) in RCA: 108] [Impact Index Per Article: 13.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 01/28/2016] [Revised: 04/06/2016] [Accepted: 04/06/2016] [Indexed: 01/03/2023]
Abstract
Heterogeneity is a key feature of all psychiatric disorders that manifests on many levels, including symptoms, disease course, and biological underpinnings. These form a substantial barrier to understanding disease mechanisms and developing effective, personalized treatments. In response, many studies have aimed to stratify psychiatric disorders, aiming to find more consistent subgroups on the basis of many types of data. Such approaches have received renewed interest after recent research initiatives, such as the National Institute of Mental Health Research Domain Criteria and the European Roadmap for Mental Health Research, both of which emphasize finding stratifications that are based on biological systems and that cut across current classifications. We first introduce the basic concepts for stratifying psychiatric disorders and then provide a methodologically oriented and critical review of the existing literature. This shows that the predominant clustering approach that aims to subdivide clinical populations into more coherent subgroups has made a useful contribution but is heavily dependent on the type of data used; it has produced many different ways to subgroup the disorders we review, but for most disorders it has not converged on a consistent set of subgroups. We highlight problems with current approaches that are not widely recognized and discuss the importance of validation to ensure that the derived subgroups index clinically relevant variation. Finally, we review emerging techniques-such as those that estimate normative models for mappings between biology and behavior-that provide new ways to parse the heterogeneity underlying psychiatric disorders and evaluate all methods to meeting the objectives of such as the National Institute of Mental Health Research Domain Criteria and Roadmap for Mental Health Research.
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Affiliation(s)
- Andre F. Marquand
- Donders Centre for Cognitive Neuroimaging, Donders Institute for Brain, Cognition and Behaviour, Radboud University, Nijmegen
- Department of Cognitive Neuroscience , Radboud University Medical Centre, Nijmegen
- Department of Neuroimaging (AFM), Centre for Neuroimaging Sciences, Institute of Psychiatry, King’s College London, London
| | - Thomas Wolfers
- Donders Centre for Cognitive Neuroimaging, Donders Institute for Brain, Cognition and Behaviour, Radboud University, Nijmegen
| | - Maarten Mennes
- Donders Centre for Cognitive Neuroimaging, Donders Institute for Brain, Cognition and Behaviour, Radboud University, Nijmegen
| | - Jan Buitelaar
- Donders Centre for Cognitive Neuroimaging, Donders Institute for Brain, Cognition and Behaviour, Radboud University, Nijmegen
- Department of Cognitive Neuroscience , Radboud University Medical Centre, Nijmegen
- Karakter Child and Adolescent Psychiatric University Centre, Nijmegen, The Netherlands
| | - Christian F. Beckmann
- Donders Centre for Cognitive Neuroimaging, Donders Institute for Brain, Cognition and Behaviour, Radboud University, Nijmegen
- Department of Cognitive Neuroscience , Radboud University Medical Centre, Nijmegen
- Oxford Centre for Functional Magnetic Resonance Imaging of the Brain (CFB), University of Oxford, London, United Kingdom
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19
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Panta SR, Wang R, Fries J, Kalyanam R, Speer N, Banich M, Kiehl K, King M, Milham M, Wager TD, Turner JA, Plis SM, Calhoun VD. A Tool for Interactive Data Visualization: Application to Over 10,000 Brain Imaging and Phantom MRI Data Sets. Front Neuroinform 2016; 10:9. [PMID: 27014049 PMCID: PMC4791544 DOI: 10.3389/fninf.2016.00009] [Citation(s) in RCA: 15] [Impact Index Per Article: 1.9] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/14/2015] [Accepted: 02/22/2016] [Indexed: 11/21/2022] Open
Abstract
In this paper we propose a web-based approach for quick visualization of big data from brain magnetic resonance imaging (MRI) scans using a combination of an automated image capture and processing system, nonlinear embedding, and interactive data visualization tools. We draw upon thousands of MRI scans captured via the COllaborative Imaging and Neuroinformatics Suite (COINS). We then interface the output of several analysis pipelines based on structural and functional data to a t-distributed stochastic neighbor embedding (t-SNE) algorithm which reduces the number of dimensions for each scan in the input data set to two dimensions while preserving the local structure of data sets. Finally, we interactively display the output of this approach via a web-page, based on data driven documents (D3) JavaScript library. Two distinct approaches were used to visualize the data. In the first approach, we computed multiple quality control (QC) values from pre-processed data, which were used as inputs to the t-SNE algorithm. This approach helps in assessing the quality of each data set relative to others. In the second case, computed variables of interest (e.g., brain volume or voxel values from segmented gray matter images) were used as inputs to the t-SNE algorithm. This approach helps in identifying interesting patterns in the data sets. We demonstrate these approaches using multiple examples from over 10,000 data sets including (1) quality control measures calculated from phantom data over time, (2) quality control data from human functional MRI data across various studies, scanners, sites, (3) volumetric and density measures from human structural MRI data across various studies, scanners and sites. Results from (1) and (2) show the potential of our approach to combine t-SNE data reduction with interactive color coding of variables of interest to quickly identify visually unique clusters of data (i.e., data sets with poor QC, clustering of data by site) quickly. Results from (3) demonstrate interesting patterns of gray matter and volume, and evaluate how they map onto variables including scanners, age, and gender. In sum, the proposed approach allows researchers to rapidly identify and extract meaningful information from big data sets. Such tools are becoming increasingly important as datasets grow larger.
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Affiliation(s)
- Sandeep R Panta
- The Mind Research Network and Lovelace Biomedical and Environmental Research Institute Albuquerque, NM, USA
| | - Runtang Wang
- The Mind Research Network and Lovelace Biomedical and Environmental Research Institute Albuquerque, NM, USA
| | - Jill Fries
- The Mind Research Network and Lovelace Biomedical and Environmental Research Institute Albuquerque, NM, USA
| | - Ravi Kalyanam
- The Mind Research Network and Lovelace Biomedical and Environmental Research Institute Albuquerque, NM, USA
| | - Nicole Speer
- Intermountain Neuroimaging Consortium, University of Boulder Colorado Boulder, CO, USA
| | - Marie Banich
- Intermountain Neuroimaging Consortium, University of Boulder Colorado Boulder, CO, USA
| | - Kent Kiehl
- The Mind Research Network and Lovelace Biomedical and Environmental Research InstituteAlbuquerque, NM, USA; Department of Psychology, University of New MexicoAlbuquerque, NM, USA
| | - Margaret King
- The Mind Research Network and Lovelace Biomedical and Environmental Research Institute Albuquerque, NM, USA
| | - Michael Milham
- The Child Mind Institute and The Nathan Kline Institute New York, NY, USA
| | - Tor D Wager
- Intermountain Neuroimaging Consortium, University of Boulder Colorado Boulder, CO, USA
| | - Jessica A Turner
- Department of Psychology, Georgia Tech University Atlanta, GA, USA
| | - Sergey M Plis
- The Mind Research Network and Lovelace Biomedical and Environmental Research InstituteAlbuquerque, NM, USA; Department of Electrical & Computer Engineering, University of New MexicoAlbuquerque, NM, USA
| | - Vince D Calhoun
- The Mind Research Network and Lovelace Biomedical and Environmental Research InstituteAlbuquerque, NM, USA; Department of Electrical & Computer Engineering, University of New MexicoAlbuquerque, NM, USA
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20
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Du Y, Pearlson GD, Liu J, Sui J, Yu Q, He H, Castro E, Calhoun VD. A group ICA based framework for evaluating resting fMRI markers when disease categories are unclear: application to schizophrenia, bipolar, and schizoaffective disorders. Neuroimage 2015. [PMID: 26216278 DOI: 10.1016/j.neuroimage.2015.07.054] [Citation(s) in RCA: 105] [Impact Index Per Article: 11.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/23/2022] Open
Abstract
Schizophrenia (SZ), bipolar disorder (BP) and schizoaffective disorder (SAD) share some common symptoms, and there is still a debate about whether SAD is an independent category. To the best of our knowledge, no study has been done to differentiate these three disorders or to investigate the distinction of SAD as an independent category using fMRI data. This study is aimed to explore biomarkers from resting-state fMRI networks for differentiating these disorders and investigate the relationship among these disorders based on fMRI networks with an emphasis on SAD. Firstly, a novel group ICA method, group information guided independent component analysis (GIG-ICA), was applied to extract subject-specific brain networks from fMRI data of 20 healthy controls (HC), 20 SZ patients, 20 BP patients, 20 patients suffering from SAD with manic episodes (SADM), and 13 patients suffering from SAD with depressive episodes exclusively (SADD). Then, five-level one-way analysis of covariance and multiclass support vector machine recursive feature elimination were employed to identify discriminative regions from the networks. Subsequently, the t-distributed stochastic neighbor embedding (t-SNE) projection and the hierarchical clustering were implemented to investigate the relationship among those groups. Finally, to evaluate the generalization ability, 16 new subjects were classified based on the found regions and the trained model using original 93 subjects. Results show that the discriminative regions mainly included frontal, parietal, precuneus, cingulate, supplementary motor, cerebellar, insula and supramarginal cortices, which performed well in distinguishing different groups. SADM and SADD were the most similar to each other, although SADD had greater similarity to SZ compared to other groups, which indicates that SAD may be an independent category. BP was closer to HC compared with other psychotic disorders. In summary, resting-state fMRI brain networks extracted via GIG-ICA provide a promising potential to differentiate SZ, BP, and SAD.
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Affiliation(s)
- Yuhui Du
- The Mind Research Network & LBERI, Albuquerque, NM, USA; School of Information and Communication Engineering, North University of China, Taiyuan, China.
| | - Godfrey D Pearlson
- Department of Psychiatry, Yale University, New Haven, CT, USA; Department of Neurobiology, Yale University, New Haven, CT, USA; Olin Neuropsychiatry Research Center, Institute of Living, Hartford, CT, USA
| | - Jingyu Liu
- The Mind Research Network & LBERI, Albuquerque, NM, USA; Department of Electrical and Computer Engineering, University of New Mexico, Albuquerque, NM, USA
| | - Jing Sui
- The Mind Research Network & LBERI, Albuquerque, NM, USA; Brainnetome Center and National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, Beijing, China
| | - Qingbao Yu
- The Mind Research Network & LBERI, Albuquerque, NM, USA
| | - Hao He
- The Mind Research Network & LBERI, Albuquerque, NM, USA; Department of Electrical and Computer Engineering, University of New Mexico, Albuquerque, NM, USA
| | | | - Vince D Calhoun
- The Mind Research Network & LBERI, Albuquerque, NM, USA; Department of Psychiatry, Yale University, New Haven, CT, USA; Department of Electrical and Computer Engineering, University of New Mexico, Albuquerque, NM, USA
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