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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.
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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
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2
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Chen Z, Liu X, Yang Q, Wang YJ, Miao K, Gong Z, Yu Y, Leonov A, Liu C, Feng Z, Chuan-Peng H. Evaluation of Risk of Bias in Neuroimaging-Based Artificial Intelligence Models for Psychiatric Diagnosis: A Systematic Review. JAMA Netw Open 2023; 6:e231671. [PMID: 36877519 PMCID: PMC9989906 DOI: 10.1001/jamanetworkopen.2023.1671] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 03/07/2023] Open
Abstract
IMPORTANCE Neuroimaging-based artificial intelligence (AI) diagnostic models have proliferated in psychiatry. However, their clinical applicability and reporting quality (ie, feasibility) for clinical practice have not been systematically evaluated. OBJECTIVE To systematically assess the risk of bias (ROB) and reporting quality of neuroimaging-based AI models for psychiatric diagnosis. EVIDENCE REVIEW PubMed was searched for peer-reviewed, full-length articles published between January 1, 1990, and March 16, 2022. Studies aimed at developing or validating neuroimaging-based AI models for clinical diagnosis of psychiatric disorders were included. Reference lists were further searched for suitable original studies. Data extraction followed the CHARMS (Checklist for Critical Appraisal and Data Extraction for Systematic Reviews of Prediction Modeling Studies) and PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-analyses) guidelines. A closed-loop cross-sequential design was used for quality control. The PROBAST (Prediction Model Risk of Bias Assessment Tool) and modified CLEAR (Checklist for Evaluation of Image-Based Artificial Intelligence Reports) benchmarks were used to systematically evaluate ROB and reporting quality. FINDINGS A total of 517 studies presenting 555 AI models were included and evaluated. Of these models, 461 (83.1%; 95% CI, 80.0%-86.2%) were rated as having a high overall ROB based on the PROBAST. The ROB was particular high in the analysis domain, including inadequate sample size (398 of 555 models [71.7%; 95% CI, 68.0%-75.6%]), poor model performance examination (with 100% of models lacking calibration examination), and lack of handling data complexity (550 of 555 models [99.1%; 95% CI, 98.3%-99.9%]). None of the AI models was perceived to be applicable to clinical practices. Overall reporting completeness (ie, number of reported items/number of total items) for the AI models was 61.2% (95% CI, 60.6%-61.8%), and the completeness was poorest for the technical assessment domain with 39.9% (95% CI, 38.8%-41.1%). CONCLUSIONS AND RELEVANCE This systematic review found that the clinical applicability and feasibility of neuroimaging-based AI models for psychiatric diagnosis were challenged by a high ROB and poor reporting quality. Particularly in the analysis domain, ROB in AI diagnostic models should be addressed before clinical application.
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Affiliation(s)
- Zhiyi Chen
- School of Psychology, Third Military Medical University, Chongqing, China
- Experimental Research Center for Medical and Psychological Science, Third Military Medical University, Chongqing, China
| | - Xuerong Liu
- School of Psychology, Third Military Medical University, Chongqing, China
- Experimental Research Center for Medical and Psychological Science, Third Military Medical University, Chongqing, China
| | - Qingwu Yang
- Department of Neurology, Daping Hospital, Third Military Medical University, Chongqing, China
| | - Yan-Jiang Wang
- Department of Neurology, Daping Hospital, Third Military Medical University, Chongqing, China
| | - Kuan Miao
- School of Psychology, Third Military Medical University, Chongqing, China
- Experimental Research Center for Medical and Psychological Science, Third Military Medical University, Chongqing, China
| | - Zheng Gong
- School of Psychology, Third Military Medical University, Chongqing, China
- Experimental Research Center for Medical and Psychological Science, Third Military Medical University, Chongqing, China
| | - Yang Yu
- School of Psychology, Third Military Medical University, Chongqing, China
| | - Artemiy Leonov
- Department of Psychology, Clark University, Worcester, Massachusetts
| | - Chunlei Liu
- School of Psychology, Qufu Normal University, Qufu, China
| | - Zhengzhi Feng
- School of Psychology, Third Military Medical University, Chongqing, China
- Experimental Research Center for Medical and Psychological Science, Third Military Medical University, Chongqing, China
| | - Hu Chuan-Peng
- School of Psychology, Nanjing Normal University, Nanjing, China
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3
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Kim JI, Bang S, Yang JJ, Kwon H, Jang S, Roh S, Kim SH, Kim MJ, Lee HJ, Lee JM, Kim BN. Classification of Preschoolers with Low-Functioning Autism Spectrum Disorder Using Multimodal MRI Data. J Autism Dev Disord 2023; 53:25-37. [PMID: 34984638 DOI: 10.1007/s10803-021-05368-z] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 11/05/2021] [Indexed: 02/03/2023]
Abstract
Multimodal imaging studies targeting preschoolers and low-functioning autism spectrum disorder (ASD) patients are scarce. We applied machine learning classifiers to parameters from T1-weighted MRI and DTI data of 58 children with ASD (age 3-6 years) and 48 typically developing controls (TDC). Classification performance reached an accuracy, sensitivity, and specificity of 88.8%, 93.0%, and 83.8%, respectively. The most prominent features were the cortical thickness of the right inferior occipital gyrus, mean diffusivity of the middle cerebellar peduncle, and nodal efficiency of the left posterior cingulate gyrus. Machine learning-based analysis of MRI data was useful in distinguishing low-functioning ASD preschoolers from TDCs. Combination of T1 and DTI improved classification accuracy about 10%, and large-scale multi-modal MRI studies are warranted for external validation.
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Affiliation(s)
- Johanna Inhyang Kim
- Department of Psychiatry, Hanyang University Medical Center, 222-1 Wangsimni-ro, Sungdong-gu, Seoul, 04763, Republic of Korea
| | - Sungkyu Bang
- Department of Biomedical Engineering, Hanyang University, 222 Wangsimni-ro, Sungdong-gu, Seoul, 04763, Republic of Korea
| | - Jin-Ju Yang
- Department of Biomedical Engineering, Hanyang University, 222 Wangsimni-ro, Sungdong-gu, Seoul, 04763, Republic of Korea
| | - Heejin Kwon
- Department of Psychology, Yonsei University, 50 Yonsei-ro, Seodaemun-gu, Seoul, 02722, Republic of Korea
| | - Soomin Jang
- Department of Psychiatry, Seoul National University College of Medicine, 101 Daehak-ro, Jongno-gu, Seoul, 03080, Republic of Korea
| | - Sungwon Roh
- Department of Psychiatry, Hanyang University Medical Center, 222-1 Wangsimni-ro, Sungdong-gu, Seoul, 04763, Republic of Korea
- Department of Psychiatry, Hanyang University College of Medicine, 222 Wangsimni-ro, Seongdong-gu, Seoul, 04763, Republic of Korea
| | - Seok Hyeon Kim
- Department of Psychiatry, Hanyang University Medical Center, 222-1 Wangsimni-ro, Sungdong-gu, Seoul, 04763, Republic of Korea
- Department of Psychiatry, Hanyang University College of Medicine, 222 Wangsimni-ro, Seongdong-gu, Seoul, 04763, Republic of Korea
| | - Mi Jung Kim
- Department of Rehabilitation Medicine, Hanyang University College of Medicine, 222 Wangsimni-ro, Sungdong-gu, Seoul, 04763, Republic of Korea
| | - Hyun Ju Lee
- Department of Pediatrics, Hanyang University College of Medicine, 222 Wangsimni-ro, Sungdong-gu, Seoul, 04763, Republic of Korea
| | - Jong-Min Lee
- Department of Biomedical Engineering, Hanyang University, 222 Wangsimni-ro, Sungdong-gu, Seoul, 04763, Republic of Korea.
| | - Bung-Nyun Kim
- Department of Psychiatry, Seoul National University College of Medicine, 101 Daehak-ro, Jongno-gu, Seoul, 03080, Republic of Korea.
- Department of Psychiatry and Behavioral Science, Seoul National University College of Medicine, 101 Daehak-no, Chongno-gu, Seoul, 03080, Republic of Korea.
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4
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Moridian P, Ghassemi N, Jafari M, Salloum-Asfar S, Sadeghi D, Khodatars M, Shoeibi A, Khosravi A, Ling SH, Subasi A, Alizadehsani R, Gorriz JM, Abdulla SA, Acharya UR. Automatic autism spectrum disorder detection using artificial intelligence methods with MRI neuroimaging: A review. Front Mol Neurosci 2022; 15:999605. [PMID: 36267703 PMCID: PMC9577321 DOI: 10.3389/fnmol.2022.999605] [Citation(s) in RCA: 10] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/21/2022] [Accepted: 08/09/2022] [Indexed: 12/04/2022] Open
Abstract
Autism spectrum disorder (ASD) is a brain condition characterized by diverse signs and symptoms that appear in early childhood. ASD is also associated with communication deficits and repetitive behavior in affected individuals. Various ASD detection methods have been developed, including neuroimaging modalities and psychological tests. Among these methods, magnetic resonance imaging (MRI) imaging modalities are of paramount importance to physicians. Clinicians rely on MRI modalities to diagnose ASD accurately. The MRI modalities are non-invasive methods that include functional (fMRI) and structural (sMRI) neuroimaging methods. However, diagnosing ASD with fMRI and sMRI for specialists is often laborious and time-consuming; therefore, several computer-aided design systems (CADS) based on artificial intelligence (AI) have been developed to assist specialist physicians. Conventional machine learning (ML) and deep learning (DL) are the most popular schemes of AI used for diagnosing ASD. This study aims to review the automated detection of ASD using AI. We review several CADS that have been developed using ML techniques for the automated diagnosis of ASD using MRI modalities. There has been very limited work on the use of DL techniques to develop automated diagnostic models for ASD. A summary of the studies developed using DL is provided in the Supplementary Appendix. Then, the challenges encountered during the automated diagnosis of ASD using MRI and AI techniques are described in detail. Additionally, a graphical comparison of studies using ML and DL to diagnose ASD automatically is discussed. We suggest future approaches to detecting ASDs using AI techniques and MRI neuroimaging.
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Affiliation(s)
- Parisa Moridian
- Faculty of Engineering, Science and Research Branch, Islamic Azad University, Tehran, Iran
| | - Navid Ghassemi
- Department of Computer Engineering, Ferdowsi University of Mashhad, Mashhad, Iran
| | - Mahboobeh Jafari
- Faculty of Electrical and Computer Engineering, Semnan University, Semnan, Iran
| | - Salam Salloum-Asfar
- Neurological Disorders Research Center, Qatar Biomedical Research Institute, Hamad Bin Khalifa University, Qatar Foundation, Doha, Qatar
| | - Delaram Sadeghi
- Department of Medical Engineering, Mashhad Branch, Islamic Azad University, Mashhad, Iran
| | - Marjane Khodatars
- Department of Medical Engineering, Mashhad Branch, Islamic Azad University, Mashhad, Iran
| | - Afshin Shoeibi
- Data Science and Computational Intelligence Institute, University of Granada, Granada, Spain
| | - Abbas Khosravi
- Institute for Intelligent Systems Research and Innovation (IISRI), Deakin University, Geelong, VIC, Australia
| | - Sai Ho Ling
- Faculty of Engineering and IT, University of Technology Sydney (UTS), Ultimo, NSW, Australia
| | - Abdulhamit Subasi
- Faculty of Medicine, Institute of Biomedicine, University of Turku, Turku, Finland
- Department of Computer Science, College of Engineering, Effat University, Jeddah, Saudi Arabia
| | - Roohallah Alizadehsani
- Institute for Intelligent Systems Research and Innovation (IISRI), Deakin University, Geelong, VIC, Australia
| | - Juan M. Gorriz
- Data Science and Computational Intelligence Institute, University of Granada, Granada, Spain
| | - Sara A. Abdulla
- Neurological Disorders Research Center, Qatar Biomedical Research Institute, Hamad Bin Khalifa University, Qatar Foundation, Doha, Qatar
| | - U. Rajendra Acharya
- Ngee Ann Polytechnic, Singapore, Singapore
- Department of Biomedical Informatics and Medical Engineering, Asia University, Taichung, Taiwan
- Department of Biomedical Engineering, School of Science and Technology, Singapore University of Social Sciences, Singapore, Singapore
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5
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Del Casale A, Ferracuti S, Alcibiade A, Simone S, Modesti MN, Pompili M. Neuroanatomical correlates of autism spectrum disorders: A meta-analysis of structural magnetic resonance imaging (MRI) studies. Psychiatry Res Neuroimaging 2022; 325:111516. [PMID: 35882091 DOI: 10.1016/j.pscychresns.2022.111516] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/05/2022] [Revised: 06/21/2022] [Accepted: 07/19/2022] [Indexed: 11/16/2022]
Abstract
Autism spectrum disorders (ASD) are neurodevelopmental disorders correlated to various neuroanatomical modifications. We aimed to identify neuroanatomical changes assessed in magnetic resonance imaging (MRI) studies of autism spectrum disorder (ASD) through Activation Likelihood Estimate (ALE) meta-analysis. We included 19 peer-reviewed magnetic resonance imaging (MRI) studies that analyzed cortical volume in patients with ASD compared to healthy control subjects (HCs). The between-group analyses comparing subjects with ASD to HCs showed a volumetric reduction of a large cluster in the right brain, including the uncus/amygdala, parahippocampal gyrus, and entorhinal cortex, and putamen. The anomalies are primarily found in the right hemisphere, involved in social cognitive function, particularly impaired in ASD. These results correlate with several clinical aspects of ASD. These volumetric alterations can be considered a major correlate of disease in the context of multifactorial etiology. Further studies on brain lateralization in ASD are needed, considering the clinical phenotype variability of these disorders.
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Affiliation(s)
- Antonio Del Casale
- Department of Dynamic and Clinical Psychology, and Health Studies; Faculty of Medicine and Psychology; Sapienza University of Rome, Italy.
| | - Stefano Ferracuti
- Department of Human Neuroscience; Faculty of Medicine and Dentistry; Sapienza University of Rome, Italy
| | | | - Sara Simone
- Faculty of Medicine and Psychology; Sapienza University of Rome, Italy
| | | | - Maurizio Pompili
- Department of Neuroscience, Mental Health, and Sensory Organs (NESMOS); Faculty of Medicine and Psychology; Sapienza University of Rome, Italy
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6
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Sader M, Williams JHG, Waiter GD. A meta-analytic investigation of grey matter differences in anorexia nervosa and autism spectrum disorder. EUROPEAN EATING DISORDERS REVIEW 2022; 30:560-579. [PMID: 35526083 PMCID: PMC9543727 DOI: 10.1002/erv.2915] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/17/2022] [Accepted: 04/21/2022] [Indexed: 11/11/2022]
Abstract
Recent research reports Anorexia Nervosa (AN) to be highly dependent upon neurobiological function. Some behaviours, particularly concerning food selectivity are found in populations with both Autism Spectrum Disorder (ASD) and AN, and there is a proportionally elevated number of anorexic patients exhibiting symptoms of ASD. We performed a systematic review of structural MRI literature with the aim of identifying common structural neural correlates common to both AN and ASD. Across 46 ASD publications, a meta‐analysis of volumetric differences between ASD and healthy controls revealed no consistently affected brain regions. Meta‐analysis of 23 AN publications revealed increased volume within the orbitofrontal cortex and medial temporal lobe, and adult‐only AN literature revealed differences within the genu of the anterior cingulate cortex. The changes are consistent with alterations in flexible reward‐related learning and episodic memory reported in neuropsychological studies. There was no structural overlap between ASD and AN. Findings suggest no consistent neuroanatomical abnormality associated with ASD, and evidence is lacking to suggest that reported behavioural similarities between those with AN and ASD are due to neuroanatomical structural similarities. Findings related to neuroanatomical structure in AN/ASD demonstrate overlap and require revisiting. Meta‐analytic findings show structural increase/decrease versus healthy controls (LPFC/MTL/OFC) in AN, but no clusters found in ASD. The neuroanatomy associated with ASD is inconsistent, but findings in AN reflect condition‐related impairment in executive function and sociocognitive behaviours.
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Affiliation(s)
- Michelle Sader
- Translational Neuroscience, Institute of Medical Sciences, University of Aberdeen, Aberdeen, UK
| | - Justin H G Williams
- Translational Neuroscience, Institute of Medical Sciences, University of Aberdeen, Aberdeen, UK
| | - Gordon D Waiter
- Biomedical Imaging Centre, Institute of Medical Sciences, University of Aberdeen, Aberdeen, UK
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7
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Dwyer D, Koutsouleris N. Annual Research Review: Translational machine learning for child and adolescent psychiatry. J Child Psychol Psychiatry 2022; 63:421-443. [PMID: 35040130 DOI: 10.1111/jcpp.13545] [Citation(s) in RCA: 10] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Accepted: 09/06/2021] [Indexed: 12/14/2022]
Abstract
Children and adolescents could benefit from the use of predictive tools that facilitate personalized diagnoses, prognoses, and treatment selection. Such tools have not yet been deployed using traditional statistical methods, potentially due to the limitations of the paradigm and the need to leverage large amounts of digital data. This review will suggest that a machine learning approach could address these challenges and is designed to introduce new readers to the background, methods, and results in the field. A rationale is first introduced followed by an outline of fundamental elements of machine learning approaches. To provide an overview of the use of the techniques in child and adolescent literature, a scoping review of broad trends is then presented. Selected studies are also highlighted in order to draw attention to research areas that are closest to translation and studies that exhibit a high degree of experimental innovation. Limitations to the research, and machine learning approaches generally, are outlined in the penultimate section highlighting issues related to sample sizes, validation, clinical utility, and ethical challenges. Finally, future directions are discussed that could enhance the possibility of clinical implementation and address specific questions relevant to the child and adolescent psychiatry. The review gives a broad overview of the machine learning paradigm in order to highlight the benefits of a shift in perspective towards practically oriented statistical solutions that aim to improve clinical care of children and adolescents.
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Affiliation(s)
- Dominic Dwyer
- Department of Psychiatry and Psychotherapy, Ludwig-Maximilian-University, Munich, Germany.,Orygen, The National Centre of Excellence in Youth Mental Health, Melbourne, Australia.,Centre for Youth Mental Health, University of Melbourne, Melbourne, Australia
| | - Nikolaos Koutsouleris
- Department of Psychiatry and Psychotherapy, Ludwig-Maximilian-University, Munich, Germany.,Max-Planck Institute of Psychiatry, Munich, Germany.,Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, United Kingdom
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8
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Duan Y, Zhao W, Luo C, Liu X, Jiang H, Tang Y, Liu C, Yao D. Identifying and Predicting Autism Spectrum Disorder Based on Multi-Site Structural MRI With Machine Learning. Front Hum Neurosci 2022; 15:765517. [PMID: 35273484 PMCID: PMC8902595 DOI: 10.3389/fnhum.2021.765517] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/27/2021] [Accepted: 12/13/2021] [Indexed: 11/13/2022] Open
Abstract
Although emerging evidence has implicated structural/functional abnormalities of patients with Autism Spectrum Disorder(ASD), definitive neuroimaging markers remain obscured due to inconsistent or incompatible findings, especially for structural imaging. Furthermore, brain differences defined by statistical analysis are difficult to implement individual prediction. The present study has employed the machine learning techniques under the unified framework in neuroimaging to identify the neuroimaging markers of patients with ASD and distinguish them from typically developing controls(TDC). To enhance the interpretability of the machine learning model, the study has processed three levels of assessments including model-level assessment, feature-level assessment, and biology-level assessment. According to these three levels assessment, the study has identified neuroimaging markers of ASD including the opercular part of bilateral inferior frontal gyrus, the orbital part of right inferior frontal gyrus, right rolandic operculum, right olfactory cortex, right gyrus rectus, right insula, left inferior parietal gyrus, bilateral supramarginal gyrus, bilateral angular gyrus, bilateral superior temporal gyrus, bilateral middle temporal gyrus, and left inferior temporal gyrus. In addition, negative correlations between the communication skill score in the Autism Diagnostic Observation Schedule (ADOS_G) and regional gray matter (GM) volume in the gyrus rectus, left middle temporal gyrus, and inferior temporal gyrus have been detected. A significant negative correlation has been found between the communication skill score in ADOS_G and the orbital part of the left inferior frontal gyrus. A negative correlation between verbal skill score and right angular gyrus and a significant negative correlation between non-verbal communication skill and right angular gyrus have been found. These findings in the study have suggested the GM alteration of ASD and correlated with the clinical severity of ASD disease symptoms. The interpretable machine learning framework gives sight to the pathophysiological mechanism of ASD but can also be extended to other diseases.
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Affiliation(s)
- YuMei Duan
- Department of Computer and Software, Chengdu Jincheng College, Chengdu, China
| | - WeiDong Zhao
- College of Computer, Chengdu University, Chengdu, China
| | - Cheng Luo
- The Key Laboratory for Neuro Information of Ministry of Education, Center for Information in Bio Medicine, High-Field Magnetic Resonance Brain Imaging Key Laboratory of Sichuan Province, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, China
| | - XiaoJu Liu
- Department of Abdominal Oncology, Cancer Center, West China Hospital, Sichuan University, Chengdu, China
| | - Hong Jiang
- Department of Neurosurgery, Rui-Jin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - YiQian Tang
- College of Computer, Chengdu University, Chengdu, China
| | - Chang Liu
- College of Computer, Chengdu University, Chengdu, China
- The Key Laboratory for Neuro Information of Ministry of Education, Center for Information in Bio Medicine, High-Field Magnetic Resonance Brain Imaging Key Laboratory of Sichuan Province, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, China
| | - DeZhong Yao
- The Key Laboratory for Neuro Information of Ministry of Education, Center for Information in Bio Medicine, High-Field Magnetic Resonance Brain Imaging Key Laboratory of Sichuan Province, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, China
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9
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Calderoni S. Sex/gender differences in children with autism spectrum disorder: A brief overview on epidemiology, symptom profile, and neuroanatomy. J Neurosci Res 2022; 101:739-750. [PMID: 35043482 DOI: 10.1002/jnr.25000] [Citation(s) in RCA: 13] [Impact Index Per Article: 6.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/28/2021] [Revised: 11/01/2021] [Accepted: 12/03/2021] [Indexed: 12/11/2022]
Abstract
Autism spectrum disorders (ASD) are a heterogeneous group of neurodevelopmental conditions whose shared core features are impairments in social interaction and communication as well as restricted patterns of behavior, interests, and activities. The significant and consistent male preponderance in ASD prevalence has historically affected the scientific knowledge of autism in females as regards, inter alia, the clinical presentation, the genetic architecture, and the structural brain underpinnings. Indeed, females with ASD are under-investigated as samples recruited for clinical research typically reflect the strong male bias of the disorder. In the last years, the study of the various aspects of sex/gender (s/g) differences in ASD is gaining increased clinical and research interest resulting in a growing number of investigations on this topic. Here, I review and discuss evidence emerged from epidemiological, clinical, and neuroimaging studies in the last decade focusing on s/g differences in children with ASD. These studies are the prerequisites for the development of assessment and treatment practices which take into consideration s/g differences in ASD. Ultimately, a better understanding of s/g differences aims at improving healthcare for both ASD males and females.
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Affiliation(s)
- Sara Calderoni
- Department of Developmental Neuroscience, IRCCS Fondazione Stella Maris, Pisa, Italy.,Department of Clinical and Experimental Medicine, University of Pisa, Pisa, Italy
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10
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Saponaro S, Giuliano A, Bellotti R, Lombardi A, Tangaro S, Oliva P, Calderoni S, Retico A. Multi-site harmonization of MRI data uncovers machine-learning discrimination capability in barely separable populations: An example from the ABIDE dataset. NEUROIMAGE: CLINICAL 2022; 35:103082. [PMID: 35700598 PMCID: PMC9198380 DOI: 10.1016/j.nicl.2022.103082] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/20/2022] [Revised: 06/06/2022] [Accepted: 06/06/2022] [Indexed: 11/25/2022] Open
Abstract
Multi-site MRI data encode confounding information which may mask case-control differences. The impact of the NeuroHarmonize method is evaluated in the ASD-control classification. We verified the successful removal of the site effect by the harmonization protocol. The increment in the classification performance is quantified after data harmonization. We identified the anatomical features that contributed to the two-class separation.
Machine Learning (ML) techniques have been widely used in Neuroimaging studies of Autism Spectrum Disorders (ASD) both to identify possible brain alterations related to this condition and to evaluate the predictive power of brain imaging modalities. The collection and public sharing of large imaging samples has favored an even greater diffusion of the use of ML-based analyses. However, multi-center data collections may suffer the batch effect, which, especially in case of Magnetic Resonance Imaging (MRI) studies, should be curated to avoid confounding effects for ML classifiers and masking biases. This is particularly important in the study of barely separable populations according to MRI data, such as subjects with ASD compared to controls with typical development (TD). Here, we show how the implementation of a harmo- nization protocol on brain structural features unlocks the case-control ML separation capability in the analysis of a multi-center MRI dataset. This effect is demonstrated on the ABIDE data collection, involving subjects encompassing a wide age range. After data harmonization, the overall ASD vs. TD discrimination capability by a Random Forest (RF) classifier improves from a very low performance (AUC = 0.58 ± 0.04) to a still low, but reasonably significant AUC = 0.67 ± 0.03. The performances of the RF classifier have been evaluated also in the age-specific subgroups of children, adolescents and adults, obtaining AUC = 0.62 ± 0.02, AUC = 0.65 ± 0.03 and AUC = 0.69 ± 0.06, respectively. Specific and consistent patterns of anatomical differences related to the ASD condition have been identified for the three different age subgroups.
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Wang S, Celebi ME, Zhang YD, Yu X, Lu S, Yao X, Zhou Q, Miguel MG, Tian Y, Gorriz JM, Tyukin I. Advances in Data Preprocessing for Biomedical Data Fusion: An Overview of the Methods, Challenges, and Prospects. INFORMATION FUSION 2021; 76:376-421. [DOI: 10.1016/j.inffus.2021.07.001] [Citation(s) in RCA: 39] [Impact Index Per Article: 13.0] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 08/30/2023]
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12
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Kraan AC, Berti A, Retico A, Baroni G, Battistoni G, Belcari N, Cerello P, Ciocca M, De Simoni M, Del Sarto D, Donetti M, Dong Y, Embriaco A, Ferrero V, Fiorina E, Fischetti M, Franciosini G, Giraudo G, Laruina F, Maestri D, Magi M, Magro G, Mancini Terracciano C, Marafini M, Mattei I, Mazzoni E, Mereu P, Mirabelli R, Mirandola A, Morrocchi M, Muraro S, Patera A, Patera V, Pennazio F, Rivetti A, Da Rocha Rolo MD, Rosso V, Sarti A, Schiavi A, Sciubba A, Solfaroli Camillocci E, Sportelli G, Tampellini S, Toppi M, Traini G, Valle SM, Valvo F, Vischioni B, Vitolo V, Wheadon R, Bisogni MG. Localization of anatomical changes in patients during proton therapy with in-beam PET monitoring: A voxel-based morphometry approach exploiting Monte Carlo simulations. Med Phys 2021; 49:23-40. [PMID: 34813083 PMCID: PMC9303286 DOI: 10.1002/mp.15336] [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: 03/06/2021] [Revised: 06/30/2021] [Accepted: 10/11/2021] [Indexed: 12/21/2022] Open
Abstract
Purpose In‐beam positron emission tomography (PET) is one of the modalities that can be used for in vivo noninvasive treatment monitoring in proton therapy. Although PET monitoring has been frequently applied for this purpose, there is still no straightforward method to translate the information obtained from the PET images into easy‐to‐interpret information for clinical personnel. The purpose of this work is to propose a statistical method for analyzing in‐beam PET monitoring images that can be used to locate, quantify, and visualize regions with possible morphological changes occurring over the course of treatment. Methods We selected a patient treated for squamous cell carcinoma (SCC) with proton therapy, to perform multiple Monte Carlo (MC) simulations of the expected PET signal at the start of treatment, and to study how the PET signal may change along the treatment course due to morphological changes. We performed voxel‐wise two‐tailed statistical tests of the simulated PET images, resembling the voxel‐based morphometry (VBM) method commonly used in neuroimaging data analysis, to locate regions with significant morphological changes and to quantify the change. Results The VBM resembling method has been successfully applied to the simulated in‐beam PET images, despite the fact that such images suffer from image artifacts and limited statistics. Three dimensional probability maps were obtained, that allowed to identify interfractional morphological changes and to visualize them superimposed on the computed tomography (CT) scan. In particular, the characteristic color patterns resulting from the two‐tailed statistical tests lend themselves to trigger alarms in case of morphological changes along the course of treatment. Conclusions The statistical method presented in this work is a promising method to apply to PET monitoring data to reveal interfractional morphological changes in patients, occurring over the course of treatment. Based on simulated in‐beam PET treatment monitoring images, we showed that with our method it was possible to correctly identify the regions that changed. Moreover we could quantify the changes, and visualize them superimposed on the CT scan. The proposed method can possibly help clinical personnel in the replanning procedure in adaptive proton therapy treatments.
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Affiliation(s)
| | - Andrea Berti
- Istituto Nazionale di Fisica Nucleare, Sezione di Pisa, Pisa, Italy.,Dipartimento di Fisica, Università di Pisa, Pisa, Italy
| | | | - Guido Baroni
- Centro Nazionale di Adroterapia Oncologica, Pavia, Italy.,Politecnico di Milano, Milano, Italy
| | | | - Nicola Belcari
- Istituto Nazionale di Fisica Nucleare, Sezione di Pisa, Pisa, Italy.,Dipartimento di Fisica, Università di Pisa, Pisa, Italy
| | | | - Mario Ciocca
- Centro Nazionale di Adroterapia Oncologica, Pavia, Italy
| | - Micol De Simoni
- Dipartimento di Fisica, Sapienza Università di Roma, Roma, Italy.,Istituto Nazionale di Fisica Nucleare, Sezione di Roma, Roma, Italy
| | - Damiano Del Sarto
- Istituto Nazionale di Fisica Nucleare, Sezione di Pisa, Pisa, Italy.,Dipartimento di Fisica, Università di Pisa, Pisa, Italy
| | - Marco Donetti
- Centro Nazionale di Adroterapia Oncologica, Pavia, Italy
| | - Yunsheng Dong
- Istituto Nazionale di Fisica Nucleare, Sezione di Milano, Milano, Italy.,Dipartimento di Fisica, Università di Milano, Milano, Italy
| | - Alessia Embriaco
- Istituto Nazionale di Fisica Nucleare, Sezione di Pavia, Pavia, Italy
| | - Veronica Ferrero
- Istituto Nazionale di Fisica Nucleare, Sezione di Torino, Torino, Italy
| | - Elisa Fiorina
- Centro Nazionale di Adroterapia Oncologica, Pavia, Italy.,Istituto Nazionale di Fisica Nucleare, Sezione di Torino, Torino, Italy
| | - Marta Fischetti
- Istituto Nazionale di Fisica Nucleare, Sezione di Roma, Roma, Italy.,Dipartimento di Scienze di Base e Applicate per l'Ingegneria, Sapienza Università di Roma, Roma, Italy
| | - Gaia Franciosini
- Dipartimento di Fisica, Sapienza Università di Roma, Roma, Italy.,Istituto Nazionale di Fisica Nucleare, Sezione di Roma, Roma, Italy
| | - Giuseppe Giraudo
- Istituto Nazionale di Fisica Nucleare, Sezione di Torino, Torino, Italy
| | - Francesco Laruina
- Istituto Nazionale di Fisica Nucleare, Sezione di Pisa, Pisa, Italy.,Dipartimento di Fisica, Università di Pisa, Pisa, Italy
| | - Davide Maestri
- Centro Nazionale di Adroterapia Oncologica, Pavia, Italy
| | - Marco Magi
- Dipartimento di Scienze di Base e Applicate per l'Ingegneria, Sapienza Università di Roma, Roma, Italy
| | - Giuseppe Magro
- Centro Nazionale di Adroterapia Oncologica, Pavia, Italy
| | - Carlo Mancini Terracciano
- Dipartimento di Fisica, Sapienza Università di Roma, Roma, Italy.,Istituto Nazionale di Fisica Nucleare, Sezione di Roma, Roma, Italy
| | - Michela Marafini
- Istituto Nazionale di Fisica Nucleare, Sezione di Roma, Roma, Italy.,Museo Storico della Fisica e Centro Studi e Ricerche "E. Fermi", Roma, Italy
| | - Ilaria Mattei
- Istituto Nazionale di Fisica Nucleare, Sezione di Milano, Milano, Italy
| | - Enrico Mazzoni
- Istituto Nazionale di Fisica Nucleare, Sezione di Milano, Milano, Italy
| | - Paolo Mereu
- Istituto Nazionale di Fisica Nucleare, Sezione di Torino, Torino, Italy
| | - Riccardo Mirabelli
- Dipartimento di Fisica, Sapienza Università di Roma, Roma, Italy.,Istituto Nazionale di Fisica Nucleare, Sezione di Roma, Roma, Italy.,Museo Storico della Fisica e Centro Studi e Ricerche "E. Fermi", Roma, Italy
| | | | - Matteo Morrocchi
- Istituto Nazionale di Fisica Nucleare, Sezione di Pisa, Pisa, Italy.,Dipartimento di Fisica, Università di Pisa, Pisa, Italy
| | - Silvia Muraro
- Istituto Nazionale di Fisica Nucleare, Sezione di Milano, Milano, Italy
| | - Alessandra Patera
- Istituto Nazionale di Fisica Nucleare, Sezione di Torino, Torino, Italy
| | - Vincenzo Patera
- Istituto Nazionale di Fisica Nucleare, Sezione di Roma, Roma, Italy.,Dipartimento di Scienze di Base e Applicate per l'Ingegneria, Sapienza Università di Roma, Roma, Italy.,Museo Storico della Fisica e Centro Studi e Ricerche "E. Fermi", Roma, Italy
| | | | - Angelo Rivetti
- Istituto Nazionale di Fisica Nucleare, Sezione di Torino, Torino, Italy
| | | | - Valeria Rosso
- Istituto Nazionale di Fisica Nucleare, Sezione di Pisa, Pisa, Italy.,Dipartimento di Fisica, Università di Pisa, Pisa, Italy
| | - Alessio Sarti
- Istituto Nazionale di Fisica Nucleare, Sezione di Roma, Roma, Italy.,Dipartimento di Scienze di Base e Applicate per l'Ingegneria, Sapienza Università di Roma, Roma, Italy.,Museo Storico della Fisica e Centro Studi e Ricerche "E. Fermi", Roma, Italy
| | - Angelo Schiavi
- Istituto Nazionale di Fisica Nucleare, Sezione di Roma, Roma, Italy.,Dipartimento di Scienze di Base e Applicate per l'Ingegneria, Sapienza Università di Roma, Roma, Italy
| | - Adalberto Sciubba
- Dipartimento di Scienze di Base e Applicate per l'Ingegneria, Sapienza Università di Roma, Roma, Italy.,Museo Storico della Fisica e Centro Studi e Ricerche "E. Fermi", Roma, Italy.,Istituto Nazionale di Fisica Nucleare, Sezione dei Laboratori di Frascati, Frascati, RM, Italy
| | - Elena Solfaroli Camillocci
- Dipartimento di Fisica, Sapienza Università di Roma, Roma, Italy.,Istituto Nazionale di Fisica Nucleare, Sezione di Roma, Roma, Italy
| | - Giancarlo Sportelli
- Istituto Nazionale di Fisica Nucleare, Sezione di Pisa, Pisa, Italy.,Dipartimento di Fisica, Università di Pisa, Pisa, Italy
| | | | - Marco Toppi
- Dipartimento di Scienze di Base e Applicate per l'Ingegneria, Sapienza Università di Roma, Roma, Italy.,Istituto Nazionale di Fisica Nucleare, Sezione dei Laboratori di Frascati, Frascati, RM, Italy
| | - Giacomo Traini
- Istituto Nazionale di Fisica Nucleare, Sezione di Roma, Roma, Italy.,Museo Storico della Fisica e Centro Studi e Ricerche "E. Fermi", Roma, Italy
| | | | | | | | - Viviana Vitolo
- Centro Nazionale di Adroterapia Oncologica, Pavia, Italy
| | - Richard Wheadon
- Istituto Nazionale di Fisica Nucleare, Sezione di Torino, Torino, Italy
| | - Maria Giuseppina Bisogni
- Istituto Nazionale di Fisica Nucleare, Sezione di Pisa, Pisa, Italy.,Dipartimento di Fisica, Università di Pisa, Pisa, Italy
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Xu M, Calhoun V, Jiang R, Yan W, Sui J. Brain imaging-based machine learning in autism spectrum disorder: methods and applications. J Neurosci Methods 2021; 361:109271. [PMID: 34174282 DOI: 10.1016/j.jneumeth.2021.109271] [Citation(s) in RCA: 14] [Impact Index Per Article: 4.7] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/04/2021] [Revised: 05/25/2021] [Accepted: 06/19/2021] [Indexed: 01/09/2023]
Abstract
Autism spectrum disorder (ASD) is a neurodevelopmental condition with early childhood onset and high heterogeneity. As the pathogenesis is still elusive, ASD diagnosis is comprised of a constellation of behavioral symptoms. Non-invasive brain imaging techniques, such as magnetic resonance imaging (MRI), provide a valuable objective measurement of the brain. Many efforts have been devoted to developing imaging-based diagnostic tools for ASD based on machine learning (ML) technologies. In this survey, we review recent advances that utilize machine learning approaches to classify individuals with and without ASD. First, we provide a brief overview of neuroimaging-based ASD classification studies, including the analysis of publications and general classification pipeline. Next, representative studies are highlighted and discussed in detail regarding different imaging modalities, methods and sample sizes. Finally, we highlight several common challenges and provide recommendations on future directions. In summary, identifying discriminative biomarkers for ASD diagnosis is challenging, and further establishing more comprehensive datasets and dissecting the individual and group heterogeneity will be critical to achieve better ADS diagnosis performance. Machine learning methods will continue to be developed and are poised to help advance the field in this regard.
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Affiliation(s)
- Ming Xu
- Brainnetome Center and National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, Beijing, China 100190; School of Artificial Intelligence, University of Chinese Academy of Sciences, Beijing, China 100049
| | - Vince Calhoun
- Tri-institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS), Georgia State University, Georgia Institute of Technology, and Emory University, Atlanta, GA, USA 30303
| | - Rongtao Jiang
- Brainnetome Center and National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, Beijing, China 100190
| | - Weizheng Yan
- Tri-institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS), Georgia State University, Georgia Institute of Technology, and Emory University, Atlanta, GA, USA 30303
| | - Jing Sui
- State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing, China 100088.
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Xie Y, Zhang X, Liu F, Qin W, Fu J, Xue K, Yu C. Brain mRNA Expression Associated with Cortical Volume Alterations in Autism Spectrum Disorder. Cell Rep 2021; 32:108137. [PMID: 32937121 DOI: 10.1016/j.celrep.2020.108137] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/01/2020] [Revised: 05/23/2020] [Accepted: 08/21/2020] [Indexed: 12/16/2022] Open
Abstract
Numerous studies report abnormal cerebral cortex volume (CCV) in autism spectrum disorder (ASD); however, genes related to CCV abnormalities in ASD remain largely unknown. Here, we identify genes associated with CCV alterations in ASD by performing spatial correlations between the gene expression of 6 donated brains and neuroimaging data from 1,404 ASD patients and 1,499 controls. Based on spatial correlations between gene expression and CCV differences from two independent meta-analyses and between gene expression and individual CCV distributions of 404 patients and 496 controls, we identify 417 genes associated with both CCV differences and individual CCV distributions. These genes are enriched for genetic association signals and genes downregulated in the ASD post-mortem brain. The expression patterns of these genes are correlated with brain activation patterns of language-related neural processes frequently impaired in ASD. These findings highlight a model whereby genetic risk impacts gene expression (downregulated), which leads to CCV alterations in ASD.
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Affiliation(s)
- Yingying Xie
- Department of Radiology and Tianjin Key Laboratory of Functional Imaging, Tianjin Medical University General Hospital, 300052 Tianjin, China
| | - Xue Zhang
- Department of Radiology and Tianjin Key Laboratory of Functional Imaging, Tianjin Medical University General Hospital, 300052 Tianjin, China
| | - Feng Liu
- Department of Radiology and Tianjin Key Laboratory of Functional Imaging, Tianjin Medical University General Hospital, 300052 Tianjin, China
| | - Wen Qin
- Department of Radiology and Tianjin Key Laboratory of Functional Imaging, Tianjin Medical University General Hospital, 300052 Tianjin, China
| | - Jilian Fu
- Department of Radiology and Tianjin Key Laboratory of Functional Imaging, Tianjin Medical University General Hospital, 300052 Tianjin, China
| | - Kaizhong Xue
- Department of Radiology and Tianjin Key Laboratory of Functional Imaging, Tianjin Medical University General Hospital, 300052 Tianjin, China
| | - Chunshui Yu
- Department of Radiology and Tianjin Key Laboratory of Functional Imaging, Tianjin Medical University General Hospital, 300052 Tianjin, China; CAS Center for Excellence in Brain Science and Intelligence Technology, Chinese Academy of Sciences, Shanghai 200031, P.R. China.
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Ursino S, Giuliano A, Martino FD, Cocuzza P, Molinari A, Stefanelli A, Giusti P, Aringhieri G, Morganti R, Neri E, Traino C, Paiar F. Incorporating dose-volume histogram parameters of swallowing organs at risk in a videofluoroscopy-based predictive model of radiation-induced dysphagia after head and neck cancer intensity-modulated radiation therapy. Strahlenther Onkol 2020; 197:209-218. [PMID: 33034672 PMCID: PMC7892680 DOI: 10.1007/s00066-020-01697-7] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/07/2020] [Accepted: 09/18/2020] [Indexed: 11/25/2022]
Abstract
Purpose To develop a videofluoroscopy-based predictive model of radiation-induced dysphagia (RID) by incorporating DVH parameters of swallowing organs at risk (SWOARs) in a machine learning analysis. Methods Videofluoroscopy (VF) was performed to assess the penetration-aspiration score (P/A) at baseline and at 6 and 12 months after RT. An RID predictive model was developed using dose to nine SWOARs and P/A-VF data at 6 and 12 months after treatment. A total of 72 dosimetric features for each patient were extracted from DVH and analyzed with linear support vector machine classification (SVC), logistic regression classification (LRC), and random forest classification (RFC). Results 38 patients were evaluable. The relevance of SWOARs DVH features emerged both at 6 months (AUC 0.82 with SVC; 0.80 with LRC; and 0.83 with RFC) and at 12 months (AUC 0.85 with SVC; 0.82 with LRC; and 0.94 with RFC). The SWOARs and the corresponding features with the highest relevance at 6 months resulted as the base of tongue (V65 and Dmean), the superior (Dmean) and medium constrictor muscle (V45, V55; V65; Dmp; Dmean; Dmax and Dmin), and the parotid glands (Dmean and Dmp). On the contrary, the features with the highest relevance at 12 months were the medium (V55; Dmin and Dmean) and inferior constrictor muscles (V55, V65 Dmin and Dmax), the glottis (V55 and Dmax), the cricopharyngeal muscle (Dmax), and the cervical esophagus (Dmax). Conclusion We trained and cross-validated an RID predictive model with high discriminative ability at both 6 and 12 months after RT. We expect to improve the predictive power of this model by enlarging the number of training datasets.
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Affiliation(s)
- Stefano Ursino
- Department of Radiation Oncology, University Hospital S. Chiara, Via Roma 55, 56100, Pisa, Italy.
| | - Alessia Giuliano
- Department of Physics, S. Luca Hospital, Via Guglielmo Lippi Francesconi 556, 55100, Lucca, Italy
| | - Fabio Di Martino
- Department of Physics, University Hospital S. Chiara, Via Roma 55, 56100, Pisa, Italy
| | - Paola Cocuzza
- Department of Radiation Oncology, University Hospital S. Chiara, Via Roma 55, 56100, Pisa, Italy
| | - Alessandro Molinari
- Department of Radiation Oncology, University Hospital S. Chiara, Via Roma 55, 56100, Pisa, Italy
- Department of Radiation Oncology, Ecomedica Institute of Clinical Research, Via Cherubini 2/4, 50053, Empoli, Italy
| | - Antonio Stefanelli
- Department of Radiation Oncology, University Hospital Cona, Via Aldo Moro 8, 44124, Ferrara, Italy
| | - Patrizia Giusti
- Department of Radiology, University Hospital S. Chiara/Cisanello, Via Roma 55/Via Paradisa 2, 56100, Pisa, Italy
| | - Giacomo Aringhieri
- Department of Radiology, University Hospital S. Chiara/Cisanello, Via Roma 55/Via Paradisa 2, 56100, Pisa, Italy
| | - Riccardo Morganti
- Department of Clinical and Experimental Medicine, Section of Statistics, Via Roma 55, 56100, Pisa, Italy
| | - Emanuele Neri
- Department of Radiology, University Hospital S. Chiara/Cisanello, Via Roma 55/Via Paradisa 2, 56100, Pisa, Italy
| | - Claudio Traino
- Department of Physics, University Hospital S. Chiara, Via Roma 55, 56100, Pisa, Italy
| | - Fabiola Paiar
- Department of Radiation Oncology, University Hospital S. Chiara, Via Roma 55, 56100, Pisa, Italy
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Nogay HS, Adeli H. Machine learning (ML) for the diagnosis of autism spectrum disorder (ASD) using brain imaging. Rev Neurosci 2020; 31:/j/revneuro.ahead-of-print/revneuro-2020-0043/revneuro-2020-0043.xml. [PMID: 32866134 DOI: 10.1515/revneuro-2020-0043] [Citation(s) in RCA: 50] [Impact Index Per Article: 12.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/25/2020] [Accepted: 07/25/2020] [Indexed: 02/24/2024]
Abstract
Autism spectrum disorder (ASD) is a neurodevelopmental incurable disorder with a long diagnostic period encountered in the early years of life. If diagnosed early, the negative effects of this disease can be reduced by starting special education early. Machine learning (ML), an increasingly ubiquitous technology, can be applied for the early diagnosis of ASD. The aim of this study is to examine and provide a comprehensive state-of-the-art review of ML research for the diagnosis of ASD based on (a) structural magnetic resonance image (MRI), (b) functional MRI and (c) hybrid imaging techniques over the past decade. The accuracy of the studies with a large number of participants is in general lower than those with fewer participants leading to the conclusion that further large-scale studies are needed. An examination of the age of the participants shows that the accuracy of the automated diagnosis of ASD is higher at a younger age range. ML technology is expected to contribute significantly to the early and rapid diagnosis of ASD in the coming years and become available to clinicians in the near future. This review is aimed to facilitate that.
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Affiliation(s)
- Hidir Selcuk Nogay
- Department of Electrical and Energy, Kayseri University, Kayseri, Turkey
- The Ohio State University, Mathematical Bioscience Institute, Columbus, OH, USA
| | - Hojjat Adeli
- Departments of Biomedical Informatics and Neuroscience, The Ohio State University, Columbus, US
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Yassin W, Nakatani H, Zhu Y, Kojima M, Owada K, Kuwabara H, Gonoi W, Aoki Y, Takao H, Natsubori T, Iwashiro N, Kasai K, Kano Y, Abe O, Yamasue H, Koike S. Machine-learning classification using neuroimaging data in schizophrenia, autism, ultra-high risk and first-episode psychosis. Transl Psychiatry 2020; 10:278. [PMID: 32801298 PMCID: PMC7429957 DOI: 10.1038/s41398-020-00965-5] [Citation(s) in RCA: 52] [Impact Index Per Article: 13.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/07/2020] [Revised: 07/06/2020] [Accepted: 07/14/2020] [Indexed: 11/09/2022] Open
Abstract
Neuropsychiatric disorders are diagnosed based on behavioral criteria, which makes the diagnosis challenging. Objective biomarkers such as neuroimaging are needed, and when coupled with machine learning, can assist the diagnostic decision and increase its reliability. Sixty-four schizophrenia, 36 autism spectrum disorder (ASD), and 106 typically developing individuals were analyzed. FreeSurfer was used to obtain the data from the participant's brain scans. Six classifiers were utilized to classify the subjects. Subsequently, 26 ultra-high risk for psychosis (UHR) and 17 first-episode psychosis (FEP) subjects were run through the trained classifiers. Lastly, the classifiers' output of the patient groups was correlated with their clinical severity. All six classifiers performed relatively well to distinguish the subject groups, especially support vector machine (SVM) and Logistic regression (LR). Cortical thickness and subcortical volume feature groups were most useful for the classification. LR and SVM were highly consistent with clinical indices of ASD. When UHR and FEP groups were run with the trained classifiers, majority of the cases were classified as schizophrenia, none as ASD. Overall, SVM and LR were the best performing classifiers. Cortical thickness and subcortical volume were most useful for the classification, compared to surface area. LR, SVM, and DT's output were clinically informative. The trained classifiers were able to help predict the diagnostic category of both UHR and FEP Individuals.
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Affiliation(s)
- Walid Yassin
- grid.26999.3d0000 0001 2151 536XDepartment of Child Neuropsychiatry, Graduate School of Medicine, The University of Tokyo, Tokyo, 113-8655 Japan
| | - Hironori Nakatani
- grid.265061.60000 0001 1516 6626Department of Information Media Technology, School of Information and Telecommunication Engineering, Tokai University, Tokyo, 108-8619 Japan
| | - Yinghan Zhu
- grid.26999.3d0000 0001 2151 536XCenter for Evolutionary Cognitive Sciences, Graduate School of Arts and Sciences, The University of Tokyo, Tokyo, 153-8902 Japan
| | - Masaki Kojima
- grid.26999.3d0000 0001 2151 536XDepartment of Child Neuropsychiatry, Graduate School of Medicine, The University of Tokyo, Tokyo, 113-8655 Japan
| | - Keiho Owada
- grid.26999.3d0000 0001 2151 536XDepartment of Child Neuropsychiatry, Graduate School of Medicine, The University of Tokyo, Tokyo, 113-8655 Japan
| | - Hitoshi Kuwabara
- grid.505613.4Department of Psychiatry, Hamamatsu University School of Medicine, Hamamatsu City, 431-3192 Japan
| | - Wataru Gonoi
- grid.26999.3d0000 0001 2151 536XDepartment of Radiology, Graduate School of Medicine, The University of Tokyo, Tokyo, 113-8655 Japan
| | - Yuta Aoki
- grid.410714.70000 0000 8864 3422Medical Institute of Developmental Disabilities Research, Showa University, Tokyo, Japan
| | - Hidemasa Takao
- grid.26999.3d0000 0001 2151 536XDepartment of Radiology, Graduate School of Medicine, The University of Tokyo, Tokyo, 113-8655 Japan
| | - Tatsunobu Natsubori
- grid.26999.3d0000 0001 2151 536XDepartment of Neuropsychiatry, Graduate School of Medicine, The University of Tokyo, Tokyo, 113-8655 Japan
| | - Norichika Iwashiro
- grid.26999.3d0000 0001 2151 536XDepartment of Neuropsychiatry, Graduate School of Medicine, The University of Tokyo, Tokyo, 113-8655 Japan
| | - Kiyoto Kasai
- grid.26999.3d0000 0001 2151 536XDepartment of Neuropsychiatry, Graduate School of Medicine, The University of Tokyo, Tokyo, 113-8655 Japan ,grid.26999.3d0000 0001 2151 536XInternational Research Center for Neurointelligence (WPI-IRCN), UTIAS, The University of Tokyo, 7-3-1 Hongo, Bunkyo-ku Tokyo, 113-8654 Japan
| | - Yukiko Kano
- grid.26999.3d0000 0001 2151 536XDepartment of Child Neuropsychiatry, Graduate School of Medicine, The University of Tokyo, Tokyo, 113-8655 Japan
| | - Osamu Abe
- grid.26999.3d0000 0001 2151 536XDepartment of Radiology, Graduate School of Medicine, The University of Tokyo, Tokyo, 113-8655 Japan
| | - Hidenori Yamasue
- Department of Psychiatry, Hamamatsu University School of Medicine, Hamamatsu City, 431-3192, Japan.
| | - Shinsuke Koike
- Center for Evolutionary Cognitive Sciences, Graduate School of Arts and Sciences, The University of Tokyo, Tokyo, 153-8902, Japan. .,Department of Neuropsychiatry, Graduate School of Medicine, The University of Tokyo, Tokyo, 113-8655, Japan. .,International Research Center for Neurointelligence (WPI-IRCN), UTIAS, The University of Tokyo, 7-3-1 Hongo, Bunkyo-ku, Tokyo, 113-8654, Japan. .,University of Tokyo Institute for Diversity & Adaptation of Human Mind (UTIDAHM), Tokyo, 153-8902, Japan. .,Center for Integrative Science of Human Behavior, Graduate School of Arts and Sciences, The University of Tokyo, 3-8-1 Komaba, Meguro-ku, Tokyo, 153-8902, Japan.
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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.
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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
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Chen T, Chen Y, Yuan M, Gerstein M, Li T, Liang H, Froehlich T, Lu L. The Development of a Practical Artificial Intelligence Tool for Diagnosing and Evaluating Autism Spectrum Disorder: Multicenter Study. JMIR Med Inform 2020; 8:e15767. [PMID: 32041690 PMCID: PMC7244998 DOI: 10.2196/15767] [Citation(s) in RCA: 15] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/06/2019] [Revised: 12/01/2019] [Accepted: 02/09/2020] [Indexed: 01/28/2023] Open
Abstract
Background Autism spectrum disorder (ASD) is a complex neurodevelopmental disorder with an unknown etiology. Early diagnosis and intervention are key to improving outcomes for patients with ASD. Structural magnetic resonance imaging (sMRI) has been widely used in clinics to facilitate the diagnosis of brain diseases such as brain tumors. However, sMRI is less frequently used to investigate neurological and psychiatric disorders, such as ASD, owing to the subtle, if any, anatomical changes of the brain. Objective This study aimed to investigate the possibility of identifying structural patterns in the brain of patients with ASD as potential biomarkers in the diagnosis and evaluation of ASD in clinics. Methods We developed a novel 2-level histogram-based morphometry (HBM) classification framework in which an algorithm based on a 3D version of the histogram of oriented gradients (HOG) was used to extract features from sMRI data. We applied this framework to distinguish patients with ASD from healthy controls using 4 datasets from the second edition of the Autism Brain Imaging Data Exchange, including the ETH Zürich (ETH), NYU Langone Medical Center: Sample 1, Oregon Health and Science University, and Stanford University (SU) sites. We used a stratified 10-fold cross-validation method to evaluate the model performance, and we applied the Naive Bayes approach to identify the predictive ASD-related brain regions based on classification contributions of each HOG feature. Results On the basis of the 3D HOG feature extraction method, our proposed HBM framework achieved an area under the curve (AUC) of >0.75 in each dataset, with the highest AUC of 0.849 in the ETH site. We compared the 3D HOG algorithm with the original 2D HOG algorithm, which showed an accuracy improvement of >4% in each dataset, with the highest improvement of 14% (6/42) in the SU site. A comparison of the 3D HOG algorithm with the scale-invariant feature transform algorithm showed an AUC improvement of >18% in each dataset. Furthermore, we identified ASD-related brain regions based on the sMRI images. Some of these regions (eg, frontal gyrus, temporal gyrus, cingulate gyrus, postcentral gyrus, precuneus, caudate, and hippocampus) are known to be implicated in ASD in prior neuroimaging literature. We also identified less well-known regions that may play unrecognized roles in ASD and be worth further investigation. Conclusions Our research suggested that it is possible to identify neuroimaging biomarkers that can distinguish patients with ASD from healthy controls based on the more cost-effective sMRI images of the brain. We also demonstrated the potential of applying data-driven artificial intelligence technology in the clinical setting of neurological and psychiatric disorders, which usually harbor subtle anatomical changes in the brain that are often invisible to the human eye.
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Affiliation(s)
- Tao Chen
- School of Information Management, Wuhan University, Wuhan, China.,School of Information Technology, Shangqiu Normal University, Shangqiu, China
| | - Ye Chen
- Division of Biomedical Informatics, Cincinnati Children's Hospital Medical Center, Cincinnati, OH, United States.,Department of Pediatrics, University of Cincinnati College of Medicine, Cincinnati, OH, United States
| | - Mengxue Yuan
- School of Information Management, Wuhan University, Wuhan, China
| | - Mark Gerstein
- Program in Neurodevelopment and Regeneration, Yale University, New Haven, CT, United States.,Department of Molecular Biophysics and Biochemistry, Yale University, New Haven, CT, United States.,Program in Computational Biology and Bioinformatics, Yale University, New Haven, CT, United States.,Department of Computer Science, Yale University, New Haven, CT, United States
| | - Tingyu Li
- Children Nutrition Research Center, Chongqing, China.,Children's Hospital of Chongqing Medical University, Chongqing, China.,Ministry of Education Key Laboratory of Child Development and Disorders, Chongqing, China.,China International Science and Technology Cooperation Base of Child Development and Critical Disorders, Chongqing, China.,Chongqing Key Laboratory of Translational Medical Research in Cognitive Development and Learning and Memory Disorders, Chongqing, China
| | - Huiying Liang
- Guangzhou Women and Children's Medical Center, Guangzhou, China.,Guangzhou Medical University, Guangzhou, China
| | - Tanya Froehlich
- Department of Pediatrics, University of Cincinnati College of Medicine, Cincinnati, OH, United States.,Division of Developmental and Behavioral Pediatrics, Cincinnati Children's Hospital Medical Center, Cincinnati, OH, United States
| | - Long Lu
- School of Information Management, Wuhan University, Wuhan, China.,Division of Biomedical Informatics, Cincinnati Children's Hospital Medical Center, Cincinnati, OH, United States.,Department of Pediatrics, University of Cincinnati College of Medicine, Cincinnati, OH, United States
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20
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Georgescu AL, Koehler JC, Weiske J, Vogeley K, Koutsouleris N, Falter-Wagner C. Machine Learning to Study Social Interaction Difficulties in ASD. Front Robot AI 2019; 6:132. [PMID: 33501147 PMCID: PMC7805744 DOI: 10.3389/frobt.2019.00132] [Citation(s) in RCA: 14] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/03/2019] [Accepted: 11/13/2019] [Indexed: 11/27/2022] Open
Abstract
Autism Spectrum Disorder (ASD) is a spectrum of neurodevelopmental conditions characterized by difficulties in social communication and social interaction as well as repetitive behaviors and restricted interests. Prevalence rates have been rising, and existing diagnostic methods are both extremely time and labor consuming. There is an urgent need for more economic and objective automatized diagnostic tools that are independent of language and experience of the diagnostician and that can help deal with the complexity of the autistic phenotype. Technological advancements in machine learning are offering a potential solution, and several studies have employed computational approaches to classify ASD based on phenomenological, behavioral or neuroimaging data. Despite of being at the core of ASD diagnosis and having the potential to be used as a behavioral marker for machine learning algorithms, only recently have movement parameters been used as features in machine learning classification approaches. In a proof-of-principle analysis of data from a social interaction study we trained a classification algorithm on intrapersonal synchrony as an automatically and objectively measured phenotypic feature from 29 autistic and 29 typically developed individuals to differentiate those individuals with ASD from those without ASD. Parameters included nonverbal motion energy values from 116 videos of social interactions. As opposed to previous studies to date, our classification approach has been applied to non-verbal behavior objectively captured during naturalistic and complex interactions with a real human interaction partner assuring high external validity. A machine learning approach lends itself particularly for capturing heterogeneous and complex behavior in real social interactions and will be essential in developing automatized and objective classification methods in ASD.
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Affiliation(s)
- Alexandra Livia Georgescu
- Department of Psychology, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, United Kingdom.,Department of Psychiatry and Psychotherapy, University Hospital of Cologne, Cologne, Germany
| | - Jana Christina Koehler
- Department of Psychiatry and Psychotherapy, Medical Faculty, LMU Munich, Munich, Germany
| | - Johanna Weiske
- Department of Psychiatry and Psychotherapy, Medical Faculty, LMU Munich, Munich, Germany
| | - Kai Vogeley
- Department of Psychiatry and Psychotherapy, University Hospital of Cologne, Cologne, Germany.,Institute of Neuroscience and Medicine, Cognitive Neuroscience (INM-3), Research Center Juelich, Jülich, Germany
| | - Nikolaos Koutsouleris
- Department of Psychiatry and Psychotherapy, Medical Faculty, LMU Munich, Munich, Germany
| | - Christine Falter-Wagner
- Department of Psychiatry and Psychotherapy, Medical Faculty, LMU Munich, Munich, Germany.,Institute of Medical Psychology, Medical Faculty, LMU Munich, Munich, Germany
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21
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Wolfers T, Floris DL, Dinga R, van Rooij D, Isakoglou C, Kia SM, Zabihi M, Llera A, Chowdanayaka R, Kumar VJ, Peng H, Laidi C, Batalle D, Dimitrova R, Charman T, Loth E, Lai MC, Jones E, Baumeister S, Moessnang C, Banaschewski T, Ecker C, Dumas G, O’Muircheartaigh J, Murphy D, Buitelaar JK, Marquand AF, Beckmann CF. From pattern classification to stratification: towards conceptualizing the heterogeneity of Autism Spectrum Disorder. Neurosci Biobehav Rev 2019; 104:240-254. [DOI: 10.1016/j.neubiorev.2019.07.010] [Citation(s) in RCA: 54] [Impact Index Per Article: 10.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/15/2019] [Revised: 07/10/2019] [Accepted: 07/15/2019] [Indexed: 11/17/2022]
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22
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Foti F, Piras F, Vicari S, Mandolesi L, Petrosini L, Menghini D. Observational Learning in Low-Functioning Children With Autism Spectrum Disorders: A Behavioral and Neuroimaging Study. Front Psychol 2019; 9:2737. [PMID: 30687188 PMCID: PMC6338041 DOI: 10.3389/fpsyg.2018.02737] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/07/2018] [Accepted: 12/19/2018] [Indexed: 12/31/2022] Open
Abstract
New skills may be learned from the outcomes of their own internally generated actions (experiential learning) or from the observation of the consequences of externally generated actions (observational learning). Observational learning requires the coordination of cognitive functions and the processing of social information. Due to the “social” abilities underlying observational learning, the study of this process in individuals with limited social abilities such as those affected by Autism Spectrum Disorders (ASD) is worthy of being investigated. We asked a group of 16 low-functioning young children with ASD and group of 16 sex- and mental age-matched typically developing (TD) children to build a house with a set of bricks after a video-demonstration showing an actor who built the house (observational task – OBS task) and then to build by trial and error another house (experiential task – EXP task). For ASD group, performances in learning tasks were correlated with measures of cortical thickness of specific Regions of Interest (ROI) and volume of deep gray matter structures known to be related with such kinds of learning. According to our a priori hypothesis, for OBS task we selected the following ROI: frontal lobe (pars opercularis, pars triangularis, and premotor area), parietal lobe (inferior parietal gyrus), temporal lobe (superior temporal gyrus), cerebellar hemispheres. For EXP task, we selected the following ROI: precentral frontal gyrus and superior frontal gyrus, cerebellar hemispheres, basal ganglia, thalamus. Although performances of ASD and TD children improved in both OBS and EXP tasks, children with ASD obtained lower scores of goal achievement than TD children in both learning tasks. Only in ASD group, goal achievement scores positively correlated with hyperimitations indicating that children with ASD tended to have a “copy-all” approach that facilitated the goal achievement. Moreover, the marked hyperimitative tendencies of children with ASD were positively associated with the thickness of left pars opercularis, left premotor area, and right superior temporal gyrus, areas belonging to mirror neuron system, and with the volume of both cerebellar hemispheres. These findings suggest that in children with ASD the hyperimitation can represent a learning strategy that might be related to the mirror neuron system.
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Affiliation(s)
- Francesca Foti
- Department of Medical and Surgical Sciences, Magna Græcia University of Catanzaro, Catanzaro, Italy.,IRCCS Fondazione Santa Lucia, Rome, Italy
| | | | - Stefano Vicari
- Child Neuropsychiatry Unit, Neuroscience Department, Children's Hospital Bambino Gesù, Rome, Italy
| | - Laura Mandolesi
- IRCCS Fondazione Santa Lucia, Rome, Italy.,Department of Motor Sciences and Wellness, Università degli Studi di Napoli Parthenope, Naples, Italy
| | - Laura Petrosini
- IRCCS Fondazione Santa Lucia, Rome, Italy.,Department of Psychology, Sapienza University of Rome, Rome, Italy
| | - Deny Menghini
- Child Neuropsychiatry Unit, Neuroscience Department, Children's Hospital Bambino Gesù, Rome, Italy
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23
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Bosco P, Giuliano A, Delafield-Butt J, Muratori F, Calderoni S, Retico A. Brainstem enlargement in preschool children with autism: Results from an intermethod agreement study of segmentation algorithms. Hum Brain Mapp 2018; 40:7-19. [PMID: 30184295 DOI: 10.1002/hbm.24351] [Citation(s) in RCA: 15] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/02/2018] [Revised: 08/01/2018] [Accepted: 08/01/2018] [Indexed: 12/25/2022] Open
Abstract
The intermethod agreement between automated algorithms for brainstem segmentation is investigated, focusing on the potential involvement of this structure in Autism Spectrum Disorders (ASD). Inconsistencies highlighted in previous studies on brainstem in the population with ASD may in part be a result of poor agreement in the extraction of structural features between different methods. A sample of 76 children with ASD and 76 age-, gender-, and intelligence-matched controls was considered. Volumetric analyses were performed using common tools for brain structures segmentation, namely FSL-FIRST, FreeSurfer (FS), and Advanced Normalization Tools (ANTs). For shape analysis SPHARM-MAT was employed. Intermethod agreement was quantified in terms of Pearson correlations between pairs of volumes obtained by the different methods. The degree of overlap between segmented masks was quantified in terms of the Dice index. Both Pearson correlations and Dice indices, showed poor agreement between FSL-FIRST and the other methods (ANTs and FS), which by contrast, yielded Pearson correlations greater than 0.93 and average Dice indices greater than 0.76 when compared with each other. As with volume, shape analyses exhibited discrepancies between segmentation methods, with particular differences noted between FSL-FIRST and the others (ANT and FS), with under- and over-segmentation in specific brainstem regions. These data suggest that research on brain structure alterations should cross-validate findings across multiple methods. We consistently detected an enlargement of brainstem volume in the whole sample and in the male cohort across multiple segmentation methods, a feature particularly driven by the subgroup of children with idiopathic intellectual disability associated with ASD.
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Affiliation(s)
- Paolo Bosco
- Pisa Division, INFN - National Institute for Nuclear Physics, Pisa, Italy
| | - Alessia Giuliano
- Pisa Division, INFN - National Institute for Nuclear Physics, Pisa, Italy
| | - Jonathan Delafield-Butt
- Faculty of Humanities and Social Science, University of Strathclyde, Glasgow, United Kingdom
| | - Filippo Muratori
- Department of Clinical and Experimental Medicine, University of Pisa, Pisa, Italy.,IRCCS Stella Maris Foundation, Pisa, Italy
| | - Sara Calderoni
- Department of Clinical and Experimental Medicine, University of Pisa, Pisa, Italy.,IRCCS Stella Maris Foundation, Pisa, Italy
| | - Alessandra Retico
- Pisa Division, INFN - National Institute for Nuclear Physics, Pisa, Italy
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25
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Abstract
Examining sex differences in the brain has been historically contentious but is nonetheless important for advancing mental health for both girls and boys. Unfortunately, females in biomedical research remain underrepresented in most mental health conditions including autism spectrum disorders (ASD), even though equal inclusion of females would improve treatment for girls and yield benefits to boys. This review examines sex differences in the relationship between neuroanatomy and neurogenetics of ASD. Recent findings reveal that girls diagnosed with ASD exhibit more intellectual and behavioral problems compared to their male counterparts, suggesting that girls may be less likely diagnosed in the absence of such problems or that they require a higher mutational load to meet the diagnostic criteria. Thus far, the female biased effect of chromosome 4, 5p15.33, 8p, 9p24.1, 11p12-13, 15q, and Xp22.3 and the male biased effect of 1p31.3, 5q12.3, 7q, 9q33.3, 11q13.4, 13q33.3, 16p11.2, 17q11-21, Xp22.33/Yp11.31, DRD1, NLGN3, MAOA, and SHANK1 deletion have been discovered in ASD. The SNPs of genes such as RYR2, UPP2, and the androgen receptor gene have been shown to have sex-biasing factors in both girls and boys diagnosed with ASD. These sex-related genetic factors may drive sex differences in the neuroanatomy of these girls and boys, including abnormal enlargement in temporal gray and white matter volumes, and atypical reduction in cerebellar gray matter volumes and corpus callosum fibers projecting to the anterior frontal cortex in ASD girls relative to boys. Such factors may also be responsible for the attenuation of brain sexual differentiation in adult men and women with ASD; however, much remains to be uncovered or replicated. Future research should leverage further the association between neuroanatomy and genetics in girls for an integrated and interdisciplinary understanding of ASD.
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26
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Andrews DS, Marquand A, Ecker C, McAlonan G. Using Pattern Classification to Identify Brain Imaging Markers in Autism Spectrum Disorder. Curr Top Behav Neurosci 2018; 40:413-436. [PMID: 29626339 DOI: 10.1007/7854_2018_47] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/12/2022]
Abstract
Autism spectrum disorder (ASD) is a neurodevelopmental condition characterized by deficits in social interaction and communication, as well as repetitive and restrictive behaviours. The etiological and phenotypic complexity of ASD has so far hindered the development of clinically useful biomarkers for the condition. Neuroimaging studies have been valuable in establishing a biological basis for ASD. Increasingly, neuroimaging has been combined with 'machine learning'-based pattern classification methods to make individual diagnostic predictions. Moving forward, the hope is that these techniques may not only facilitate the diagnostic process but may also aid in fractionating the ASD phenotype into more biologically homogeneous sub-groups, with defined pathophysiology, predictable outcomes and/or responses to targeted treatments and/or interventions. This review chapter will first introduce 'machine learning' and pattern recognition methods in general, with a focus on their application to diagnostic classification. It will highlight why such approaches to biomarker discovery may have advantages over more conventional analytical methods. Magnetic resonance imaging (MRI) findings of atypical brain structure, function and connectivity in ASD will be briefly reviewed before we describe how pattern recognition has been applied to generate predictive models for ASD. Last, we will discuss some limitations and pitfalls of pattern recognition analyses in ASD and consider how the field can advance beyond the prediction of binary outcomes.
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Affiliation(s)
- Derek Sayre Andrews
- The Medical Investigation of Neurodevelopmental Disorders (MIND) Institute and Department of Psychiatry and Behavioural Sciences, UC Davis School of Medicine, University of California Davis, Sacramento, CA, USA.,Department of Forensic and Neurodevelopmental Sciences, The Sackler Institute for Translational Neurodevelopment, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, UK
| | - Andre Marquand
- Donders Institute for Brain, Cognition and Behaviour, Radboud University, Nijmegen, The Netherlands.,Centre for Neuroimaging Sciences, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, UK
| | - Christine Ecker
- Department of Forensic and Neurodevelopmental Sciences, The Sackler Institute for Translational Neurodevelopment, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, UK.,Department of Child and Adolescent Psychiatry, Psychosomatics and Psychotherapy, Universitätsklinikum Frankfurt am Main, Goethe-University Frankfurt am Main, Frankfurt, Germany
| | - Grainne McAlonan
- Department of Forensic and Neurodevelopmental Sciences, The Sackler Institute for Translational Neurodevelopment, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, UK. .,South London and Maudsley NHS Foundation Trust, London, UK.
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27
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Gu Q, Zhang H, Xuan M, Luo W, Huang P, Xia S, Zhang M. Automatic Classification on Multi-Modal MRI Data for Diagnosis of the Postural Instability and Gait Difficulty Subtype of Parkinson's Disease. JOURNAL OF PARKINSONS DISEASE 2017; 6:545-56. [PMID: 27176623 DOI: 10.3233/jpd-150729] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/23/2022]
Abstract
BACKGROUND Patients with the postural instability and gait difficulty subtype (PIGD) of Parkinson's disease (PD) are a refractory challenge in clinical practice. Despite previous attempts that have been made at studying subtype-specific brain alterations across PD population, conclusive neuroimaging biomarkers on patients with the PIGD subtype are still lacking. Machine learning-based classifications are a promising tool for differential diagnosis that effectively integrate complex and multivariate data. OBJECTIVE Our present study aimed to introduce the machine learning-based automatic classification for the first time to distinguish patients with the PIGD subtype from those with the non-PIGD subtype of PD at the individual level. METHODS Fifty-two PD patients and forty-five normal controls (NCs) were recruited and underwent multi-modal MRI scans including a set of resting-state functional, 3D T1-weighted and diffusion tensor imaging sequences. By comparing the PD patients with the NCs, features that were not conducive to the subtype-specific classification were ruled out from massive brain features. We applied a support vector machine classifier with the recursive feature elimination method to multi-modal MRI data for selecting features with the best discriminating power, and evaluated the proposed classifier with the leave-one-out cross-validation. RESULTS Using this classifier, we obtained satisfactory diagnostic rates (accuracy = 92.31%, specificity = 96.97%, sensitivity = 84.21% and AUCmax = 0.9585). The diagnostic agreement evaluated by the Kappa test showed an almost perfect agreement with the existing clinical categorization (Kappa value = 0.83). CONCLUSIONS With these favorable results, our findings suggested the machine learning-based classification as an alternative technique to classifying clinical subtypes in PD.
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Affiliation(s)
- Quanquan Gu
- Department of Radiology, the Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China
| | - Huan Zhang
- Key Laboratory of Biomedical Engineering of Ministry of Education, Zhejiang University, Hangzhou, China.,Zhejiang Provincial Key Laboratory of Cardio-Cerebral Vascular Detection Technology and Medicinal Effectiveness Appraisal, Zhejiang University, Hangzhou, China
| | - Min Xuan
- Department of Radiology, the Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China
| | - Wei Luo
- Department of Neurology, 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
| | - Shunren Xia
- Key Laboratory of Biomedical Engineering of Ministry of Education, Zhejiang University, Hangzhou, China.,Zhejiang Provincial Key Laboratory of Cardio-Cerebral Vascular Detection Technology and Medicinal Effectiveness Appraisal, Zhejiang University, Hangzhou, China
| | - Minming Zhang
- Department of Radiology, the Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China
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Syed MA, Yang Z, Hu XP, Deshpande G. Investigating Brain Connectomic Alterations in Autism Using the Reproducibility of Independent Components Derived from Resting State Functional MRI Data. Front Neurosci 2017; 11:459. [PMID: 28943835 PMCID: PMC5596295 DOI: 10.3389/fnins.2017.00459] [Citation(s) in RCA: 14] [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: 01/01/2017] [Accepted: 07/31/2017] [Indexed: 11/20/2022] Open
Abstract
Significance: Autism is a developmental disorder that is currently diagnosed using behavioral tests which can be subjective. Consequently, objective non-invasive imaging biomarkers of Autism are being actively researched. The common theme emerging from previous functional magnetic resonance imaging (fMRI) studies is that Autism is characterized by alterations of fMRI-derived functional connections in certain brain networks which may provide a biomarker for objective diagnosis. However, identification of individuals with Autism solely based on these measures has not been reliable, especially when larger sample sizes are taken into consideration. Objective: We surmise that metrics derived from Autism subjects may not be highly reproducible within this group leading to poor generalizability. We hypothesize that functional brain networks that are most reproducible within Autism and healthy Control groups separately, but not when the two groups are merged, may possess the ability to distinguish effectively between the groups. Methods: In this study, we propose a "discover-confirm" scheme based upon the assessment of reproducibility of independent components obtained from resting state fMRI (discover) followed by a clustering analysis of these components to evaluate their ability to discriminate between groups in an unsupervised way (confirm). Results: We obtained cluster purity ranging from 0.695 to 0.971 in a data set of 799 subjects acquired from multiple sites, depending on how reproducible the corresponding components were in each group. Conclusion: The proposed method was able to characterize reproducibility of brain networks in Autism and could potentially be deployed in other mental disorders as well.
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Affiliation(s)
- Mohammed A. Syed
- Computer Science and Software Engineering Department, Auburn UniversityAuburn, AL, United States
| | - Zhi Yang
- Shanghai Key Laboratory of Psychotic Disorders, Shanghai Mental Health Center, Shanghai Jiao Tong University School of MedicineShanghai, China
- Brain Science and Technology Research Center, Shanghai Jiao Tong UniversityShanghai, China
| | - Xiaoping P. Hu
- The Department of Bioengineering, University of California, RiversideRiverside, CA, United States
| | - Gopikrishna Deshpande
- The Department of Electrical and Computer Engineering, AU MRI Research Center, Auburn UniversityAuburn, AL, United States
- The Department of Psychology, Auburn UniversityAuburn, AL, United States
- The Alabama Advanced Imaging Consortium at Auburn University, University of Alabama BirminghamAuburn, AL, United States
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Gray matter abnormalities in pediatric autism spectrum disorder: a meta-analysis with signed differential mapping. Eur Child Adolesc Psychiatry 2017; 26:933-945. [PMID: 28233073 DOI: 10.1007/s00787-017-0964-4] [Citation(s) in RCA: 40] [Impact Index Per Article: 5.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/01/2016] [Accepted: 02/17/2017] [Indexed: 02/05/2023]
Abstract
The gray matter abnormalities revealed by magnetic resonance imaging are inconsistent, especially in pediatric individuals with autism spectrum disorder (ASD) (age < 18 years old), a phenomenon possibly related to the core pathophysiology of ASD. The purpose of our meta-analysis was to identify and map the specific gray matter abnormalities in pediatric ASD individuals thereby exploring the potential effects of clinical and demographic characteristics of these gray matter changes. A systematic search was conducted to identify voxel-based morphometry studies in pediatric individuals with ASD. The effect-size signed differential mapping method was used to quantitatively estimate the regional gray matter abnormalities in pediatric ASD individuals. Meta-regression was used to examine the associations among age, gender, intelligence quotient, symptom severity and gray matter changes. Fifteen studies including 364 pediatric individuals with ASD (male = 282, age = 10.3 ± 4.4 years) and 377 healthy controls (male = 289, age = 10.5 ± 4.2 years) were included. Pediatric ASD individuals showed significant gray matter increases in the right angular gyrus, left superior and middle frontal gyrus, left precuneus, left inferior occipital gyrus and right inferior temporal gyrus, most of which involving the default mode network, and decreases in the left cerebellum and left postcentral gyrus. The meta-regression analysis showed that the repetitive behavior scores of the Autism Diagnostic Interview-Revised were positively associated with increased gray matter volumes in the right angular gyrus. Increased rather than decreased gray matter volume, especially involving the angular gyrus and prefrontal cortex may be the core pathophysiology in the early course of ASD.
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30
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Uddin LQ, Dajani DR, Voorhies W, Bednarz H, Kana RK. Progress and roadblocks in the search for brain-based biomarkers of autism and attention-deficit/hyperactivity disorder. Transl Psychiatry 2017; 7:e1218. [PMID: 28892073 PMCID: PMC5611731 DOI: 10.1038/tp.2017.164] [Citation(s) in RCA: 96] [Impact Index Per Article: 13.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/01/2017] [Accepted: 06/07/2017] [Indexed: 11/22/2022] Open
Abstract
Children with neurodevelopmental disorders benefit most from early interventions and treatments. The development and validation of brain-based biomarkers to aid in objective diagnosis can facilitate this important clinical aim. The objective of this review is to provide an overview of current progress in the use of neuroimaging to identify brain-based biomarkers for autism spectrum disorder (ASD) and attention-deficit/hyperactivity disorder (ADHD), two prevalent neurodevelopmental disorders. We summarize empirical work that has laid the foundation for using neuroimaging to objectively quantify brain structure and function in ways that are beginning to be used in biomarker development, noting limitations of the data currently available. The most successful machine learning methods that have been developed and applied to date are discussed. Overall, there is increasing evidence that specific features (for example, functional connectivity, gray matter volume) of brain regions comprising the salience and default mode networks can be used to discriminate ASD from typical development. Brain regions contributing to successful discrimination of ADHD from typical development appear to be more widespread, however there is initial evidence that features derived from frontal and cerebellar regions are most informative for classification. The identification of brain-based biomarkers for ASD and ADHD could potentially assist in objective diagnosis, monitoring of treatment response and prediction of outcomes for children with these neurodevelopmental disorders. At present, however, the field has yet to identify reliable and reproducible biomarkers for these disorders, and must address issues related to clinical heterogeneity, methodological standardization and cross-site validation before further progress can be achieved.
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Affiliation(s)
- L Q Uddin
- Department of Psychology, University of Miami, Coral Gables, FL, USA,Neuroscience Program, University of Miami Miller School of Medicine, Miami, FL, USA,Department of Psychology, University of Miami, P.O. Box 248185-0751, Coral Gables, FL 33124, USA. E-mail:
| | - D R Dajani
- Department of Psychology, University of Miami, Coral Gables, FL, USA
| | - W Voorhies
- Department of Psychology, University of Miami, Coral Gables, FL, USA
| | - H Bednarz
- Department of Psychology, University of Alabama at Birmingham, Birmingham, AL, USA
| | - R K Kana
- Department of Psychology, University of Alabama at Birmingham, Birmingham, AL, USA
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31
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Retico A, Arezzini S, Bosco P, Calderoni S, Ciampa A, Coscetti S, Cuomo S, De Santis L, Fabiani D, Fantacci ME, Giuliano A, Mazzoni E, Mercatali P, Miscali G, Pardini M, Prosperi M, Romano F, Tamburini E, Tosetti M, Muratori F. ARIANNA: A research environment for neuroimaging studies in autism spectrum disorders. Comput Biol Med 2017; 87:1-7. [PMID: 28544911 DOI: 10.1016/j.compbiomed.2017.05.017] [Citation(s) in RCA: 6] [Impact Index Per Article: 0.9] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/13/2017] [Revised: 05/15/2017] [Accepted: 05/15/2017] [Indexed: 01/07/2023]
Abstract
The complexity and heterogeneity of Autism Spectrum Disorders (ASD) require the implementation of dedicated analysis techniques to obtain the maximum from the interrelationship among many variables that describe affected individuals, spanning from clinical phenotypic characterization and genetic profile to structural and functional brain images. The ARIANNA project has developed a collaborative interdisciplinary research environment that is easily accessible to the community of researchers working on ASD (https://arianna.pi.infn.it). The main goals of the project are: to analyze neuroimaging data acquired in multiple sites with multivariate approaches based on machine learning; to detect structural and functional brain characteristics that allow the distinguishing of individuals with ASD from control subjects; to identify neuroimaging-based criteria to stratify the population with ASD to support the future development of personalized treatments. Secure data handling and storage are guaranteed within the project, as well as the access to fast grid/cloud-based computational resources. This paper outlines the web-based architecture, the computing infrastructure and the collaborative analysis workflows at the basis of the ARIANNA interdisciplinary working environment. It also demonstrates the full functionality of the research platform. The availability of this innovative working environment for analyzing clinical and neuroimaging information of individuals with ASD is expected to support researchers in disentangling complex data thus facilitating their interpretation.
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Affiliation(s)
- Alessandra Retico
- National Institute for Nuclear Physics (INFN), Largo Bruno Pontecorvo 3, 56127 Pisa, Italy.
| | - Silvia Arezzini
- National Institute for Nuclear Physics (INFN), Largo Bruno Pontecorvo 3, 56127 Pisa, Italy
| | - Paolo Bosco
- National Institute for Nuclear Physics (INFN), Largo Bruno Pontecorvo 3, 56127 Pisa, Italy
| | - Sara Calderoni
- IRCCS Stella Maris Foundation, Viale del Tirreno 331, 56128 Pisa, Italy; Department of Clinical and Experimental Medicine, University of Pisa, Pisa, Italy
| | - Alberto Ciampa
- National Institute for Nuclear Physics (INFN), Largo Bruno Pontecorvo 3, 56127 Pisa, Italy
| | - Simone Coscetti
- National Institute for Nuclear Physics (INFN), Largo Bruno Pontecorvo 3, 56127 Pisa, Italy
| | - Stefano Cuomo
- Institute of Legal Information Theory and Techniques (ITTIG) of the National Research Council, Via de' Barucci 20, 50127 Florence, Italy
| | | | - Dario Fabiani
- National Institute for Nuclear Physics (INFN), Largo Bruno Pontecorvo 3, 56127 Pisa, Italy
| | - Maria Evelina Fantacci
- National Institute for Nuclear Physics (INFN), Largo Bruno Pontecorvo 3, 56127 Pisa, Italy; University of Pisa, Physics Department, Largo Bruno Pontecorvo 3, 56127 Pisa, Italy
| | - Alessia Giuliano
- National Institute for Nuclear Physics (INFN), Largo Bruno Pontecorvo 3, 56127 Pisa, Italy
| | - Enrico Mazzoni
- National Institute for Nuclear Physics (INFN), Largo Bruno Pontecorvo 3, 56127 Pisa, Italy
| | - Pietro Mercatali
- Institute of Legal Information Theory and Techniques (ITTIG) of the National Research Council, Via de' Barucci 20, 50127 Florence, Italy
| | | | | | | | - Francesco Romano
- Institute of Legal Information Theory and Techniques (ITTIG) of the National Research Council, Via de' Barucci 20, 50127 Florence, Italy
| | | | - Michela Tosetti
- IRCCS Stella Maris Foundation, Viale del Tirreno 331, 56128 Pisa, Italy
| | - Filippo Muratori
- IRCCS Stella Maris Foundation, Viale del Tirreno 331, 56128 Pisa, Italy; Department of Clinical and Experimental Medicine, University of Pisa, Pisa, Italy
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Carlisi CO, Norman LJ, Lukito SS, Radua J, Mataix-Cols D, Rubia K. Comparative Multimodal Meta-analysis of Structural and Functional Brain Abnormalities in Autism Spectrum Disorder and Obsessive-Compulsive Disorder. Biol Psychiatry 2017; 82:83-102. [PMID: 27887721 DOI: 10.1016/j.biopsych.2016.10.006] [Citation(s) in RCA: 117] [Impact Index Per Article: 16.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/08/2016] [Revised: 10/03/2016] [Accepted: 10/05/2016] [Indexed: 01/17/2023]
Abstract
BACKGROUND Autism spectrum disorder (ASD) and obsessive-compulsive disorder (OCD) share inhibitory control deficits possibly underlying poor control over stereotyped and repetitive and compulsive behaviors, respectively. However, it is unclear whether these symptom profiles are mediated by common or distinct neural profiles. This comparative multimodal meta-analysis assessed shared and disorder-specific neuroanatomy and neurofunction of inhibitory functions. METHODS A comparative meta-analysis of 62 voxel-based morphometry and 26 functional magnetic resonance imaging (fMRI) studies of inhibitory control was conducted comparing gray matter volume and activation abnormalities between patients with ASD (structural MRI: 911; fMRI: 188) and OCD (structural MRI: 928; fMRI: 247) and control subjects. Multimodal meta-analysis compared groups across voxel-based morphometry and fMRI. RESULTS Both disorders shared reduced function and structure in the rostral and dorsomedial prefrontal cortex including the anterior cingulate. OCD patients had a disorder-specific increase in structure and function of left basal ganglia (BG) and insula relative to control subjects and ASD patients, who had reduced right BG and insula volumes versus OCD patients. In fMRI, ASD patients showed disorder-specific reduced left dorsolateral-prefrontal activation and reduced posterior cingulate deactivation, whereas OCD patients showed temporoparietal underactivation. CONCLUSIONS The multimodal comparative meta-analysis shows shared and disorder-specific abnormalities. Whereas the rostrodorsomedial prefrontal cortex was smaller in structure and function in both disorders, this was concomitant with increased structure and function in BG and insula in OCD patients, but a reduction in ASD patients, presumably reflecting a disorder-specific frontostriatoinsular dysregulation in OCD in the form of poor frontal control over overactive BG, and a frontostriatoinsular maldevelopment in ASD with reduced structure and function in this network. Disorder-differential mechanisms appear to drive overlapping phenotypes of inhibitory control abnormalities in patients with ASD and OCD.
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Affiliation(s)
- Christina O Carlisi
- Department of Child and Adolescent Psychiatry Institute of Psychology, Psychiatry, and Neuroscience, King's College London, London, United Kingdom
| | - Luke J Norman
- Department of Child and Adolescent Psychiatry Institute of Psychology, Psychiatry, and Neuroscience, King's College London, London, United Kingdom
| | - Steve S Lukito
- Department of Child and Adolescent Psychiatry Institute of Psychology, Psychiatry, and Neuroscience, King's College London, London, United Kingdom
| | - Joaquim Radua
- Department of Psychosis Studies, Institute of Psychology, Psychiatry, and Neuroscience, King's College London, London, United Kingdom; Department of Clinical Neuroscience, Centre for Psychiatry Research, Karolinska Institutet, Stockholm, Sweden; FIDMAG Germanes Hospitalàries, CIBERSAM, Barcelona, Spain
| | - David Mataix-Cols
- Department of Clinical Neuroscience, Centre for Psychiatry Research, Karolinska Institutet, Stockholm, Sweden
| | - Katya Rubia
- Department of Child and Adolescent Psychiatry Institute of Psychology, Psychiatry, and Neuroscience, King's College London, London, United Kingdom.
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Chen Y, Sha M, Zhao X, Ma J, Ni H, Gao W, Ming D. Automated detection of pathologic white matter alterations in Alzheimer's disease using combined diffusivity and kurtosis method. Psychiatry Res Neuroimaging 2017; 264:35-45. [PMID: 28448817 DOI: 10.1016/j.pscychresns.2017.04.004] [Citation(s) in RCA: 24] [Impact Index Per Article: 3.4] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/14/2016] [Revised: 04/01/2017] [Accepted: 04/12/2017] [Indexed: 10/19/2022]
Abstract
Diffusion tensor imaging (DTI) and diffusion kurtosis imaging (DKI) are important diffusion MRI techniques for detecting microstructure abnormities in diseases such as Alzheimer's. The advantages of DKI over DTI have been reported generally; however, the indistinct relationship between diffusivity and kurtosis has not been clearly revealed in clinical settings. In this study, we hypothesize that the combination of diffusivity and kurtosis in DKI improves the capacity of DKI to detect Alzheimer's disease compared with diffusivity or kurtosis alone. Specifically, a support vector machine-based approach was applied to combine diffusivity and kurtosis and to compare different indices datasets. Strict assessments were conducted to ensure the reliability of all classifiers. Then, data from the optimized classifiers were used to detect abnormalities. With the combination, high accuracy performances of 96.23% were obtained in 53 subjects, including 27 Alzheimer's patients. More highly scored abnormal regions were selected by the combination than alone. The results revealed that more precise diffusivity and complementary kurtosis mainly contributed to the high performance of the combination in DKI. This study provides further understanding of DKI and the relationship between diffusivity and kurtosis in pathologic white matter alterations in Alzheimer's disease.
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Affiliation(s)
- Yuanyuan Chen
- School of Electronics and Information Engineering, Tianjin University, Tianjin, China.
| | - Miao Sha
- The Neural Engineering & Rehabilitation lab, Tianjin University, Tianjin, China.
| | - Xin Zhao
- The Neural Engineering & Rehabilitation lab, Tianjin University, Tianjin, China.
| | - Jianguo Ma
- School of Electronics and Information Engineering, Tianjin University, Tianjin, China.
| | - Hongyan Ni
- Department of Radiology, Tianjin First Central Hospital, Tianjin, China.
| | - Wei Gao
- Department of Biomedical Sciences and Academic Imaging, Cedars-Sinai Medical Center, CA, USA.
| | - Dong Ming
- The Neural Engineering & Rehabilitation lab, Tianjin University, Tianjin, China.
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34
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What is the Nature of Motor Impairments in Autism, Are They Diagnostically Useful, and What Are the Implications for Intervention? CURRENT DEVELOPMENTAL DISORDERS REPORTS 2017. [DOI: 10.1007/s40474-017-0109-y] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/15/2022]
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35
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Zeestraten EA, Gudbrandsen MC, Daly E, de Schotten MT, Catani M, Dell'Acqua F, Lai MC, Ruigrok ANV, Lombardo MV, Chakrabarti B, Baron-Cohen S, Ecker C, Murphy DGM, Craig MC. Sex differences in frontal lobe connectivity in adults with autism spectrum conditions. Transl Psychiatry 2017; 7:e1090. [PMID: 28398337 PMCID: PMC5416715 DOI: 10.1038/tp.2017.9] [Citation(s) in RCA: 29] [Impact Index Per Article: 4.1] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/27/2016] [Revised: 11/28/2016] [Accepted: 11/30/2016] [Indexed: 01/04/2023] Open
Abstract
Autism spectrum conditions (ASC) are more prevalent in males than females. The biological basis of this difference remains unclear. It has been postulated that one of the primary causes of ASC is a partial disconnection of the frontal lobe from higher-order association areas during development (that is, a frontal 'disconnection syndrome'). Therefore, in the current study we investigated whether frontal connectivity differs between males and females with ASC. We recruited 98 adults with a confirmed high-functioning ASC diagnosis (61 males: aged 18-41 years; 37 females: aged 18-37 years) and 115 neurotypical controls (61 males: aged 18-45 years; 54 females: aged 18-52 years). Current ASC symptoms were evaluated using the Autism Diagnostic Observation Schedule (ADOS). Diffusion tensor imaging was performed and fractional anisotropy (FA) maps were created. Mean FA values were determined for five frontal fiber bundles and two non-frontal fiber tracts. Between-group differences in mean tract FA, as well as sex-by-diagnosis interactions were assessed. Additional analyses including ADOS scores informed us on the influence of current ASC symptom severity on frontal connectivity. We found that males with ASC had higher scores of current symptom severity than females, and had significantly lower mean FA values for all but one tract compared to controls. No differences were found between females with or without ASC. Significant sex-by-diagnosis effects were limited to the frontal tracts. Taking current ASC symptom severity scores into account did not alter the findings, although the observed power for these analyses varied. We suggest these findings of frontal connectivity abnormalities in males with ASC, but not in females with ASC, have the potential to inform us on some of the sex differences reported in the behavioral phenotype of ASC.
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Affiliation(s)
- E A Zeestraten
- Department of Forensic and Neurodevelopmental Sciences, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, UK
| | - M C Gudbrandsen
- Department of Forensic and Neurodevelopmental Sciences, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, UK
| | - E Daly
- Department of Forensic and Neurodevelopmental Sciences, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, UK
| | - M T de Schotten
- Department of Forensic and Neurodevelopmental Sciences, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, UK
| | - M Catani
- Department of Forensic and Neurodevelopmental Sciences, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, UK
| | - F Dell'Acqua
- Department of Forensic and Neurodevelopmental Sciences, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, UK
| | - M-C Lai
- Autism Research Centre, Department of Psychiatry, University of Cambridge, Cambridge, UK
- Child and Youth Mental Health Collaborative at the Centre for Addiction and Mental Health and The Hospital for Sick Children, Department of Psychiatry, University of Toronto, Toronto, ON, Canada
- Department of Psychiatry, National Taiwan University Hospital and College of Medicine, Taipei, Taiwan
| | - A N V Ruigrok
- Autism Research Centre, Department of Psychiatry, University of Cambridge, Cambridge, UK
| | - M V Lombardo
- Autism Research Centre, Department of Psychiatry, University of Cambridge, Cambridge, UK
- Department of Psychology and Center for Applied Neuroscience, University of Cyprus, Nicosia, Cyprus
| | - B Chakrabarti
- Autism Research Centre, Department of Psychiatry, University of Cambridge, Cambridge, UK
- School of Psychology and Clinical Language Sciences, Centre for Integrative Neuroscience and Neurodynamics, University of Reading, Reading, UK
| | - S Baron-Cohen
- Autism Research Centre, Department of Psychiatry, University of Cambridge, Cambridge, UK
- CLASS Clinic, Cambridgeshire and Peterborough NHS Foundation Trust, Cambridge, UK
| | - C Ecker
- Department of Forensic and Neurodevelopmental Sciences, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, UK
| | - D G M Murphy
- Department of Forensic and Neurodevelopmental Sciences, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, UK
- Sackler Institute for Translational Neurodevelopment, Institute of Psychiatry, King's College London, London, UK
| | - M C Craig
- Department of Forensic and Neurodevelopmental Sciences, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, UK
- National Autism Unit, Bethlem Royal Hospital, SLAM NHS Foundation Trust, London, UK
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Arbabshirani MR, Plis S, Sui J, Calhoun VD. Single subject prediction of brain disorders in neuroimaging: Promises and pitfalls. Neuroimage 2017; 145:137-165. [PMID: 27012503 PMCID: PMC5031516 DOI: 10.1016/j.neuroimage.2016.02.079] [Citation(s) in RCA: 504] [Impact Index Per Article: 72.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/06/2015] [Revised: 02/03/2016] [Accepted: 02/25/2016] [Indexed: 01/18/2023] Open
Abstract
Neuroimaging-based single subject prediction of brain disorders has gained increasing attention in recent years. Using a variety of neuroimaging modalities such as structural, functional and diffusion MRI, along with machine learning techniques, hundreds of studies have been carried out for accurate classification of patients with heterogeneous mental and neurodegenerative disorders such as schizophrenia and Alzheimer's disease. More than 500 studies have been published during the past quarter century on single subject prediction focused on a multiple brain disorders. In the first part of this study, we provide a survey of more than 200 reports in this field with a focus on schizophrenia, mild cognitive impairment (MCI), Alzheimer's disease (AD), depressive disorders, autism spectrum disease (ASD) and attention-deficit hyperactivity disorder (ADHD). Detailed information about those studies such as sample size, type and number of extracted features and reported accuracy are summarized and discussed. To our knowledge, this is by far the most comprehensive review of neuroimaging-based single subject prediction of brain disorders. In the second part, we present our opinion on major pitfalls of those studies from a machine learning point of view. Common biases are discussed and suggestions are provided. Moreover, emerging trends such as decentralized data sharing, multimodal brain imaging, differential diagnosis, disease subtype classification and deep learning are also discussed. Based on this survey, there is extensive evidence showing the great potential of neuroimaging data for single subject prediction of various disorders. However, the main bottleneck of this exciting field is still the limited sample size, which could be potentially addressed by modern data sharing models such as the ones discussed in this paper. Emerging big data technologies and advanced data-intensive machine learning methodologies such as deep learning have coincided with an increasing need for accurate, robust and generalizable single subject prediction of brain disorders during an exciting time. In this report, we survey the past and offer some opinions regarding the road ahead.
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Affiliation(s)
- Mohammad R Arbabshirani
- The Mind Research Network, Albuquerque, NM 87106, USA; Geisinger Health System, Danville, PA 17822, USA
| | - Sergey Plis
- The Mind Research Network, Albuquerque, NM 87106, USA
| | - Jing Sui
- The Mind Research Network, Albuquerque, NM 87106, USA; Brainnetome Center and National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China
| | - Vince D Calhoun
- The Mind Research Network, Albuquerque, NM 87106, USA; Department of ECE, University of New Mexico, Albuquerque, NM, USA
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37
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Michael AM, Evans E, Moore GJ. Influence of Group on Individual Subject Maps in SPM Voxel Based Morphometry. Front Neurosci 2016; 10:522. [PMID: 27994534 PMCID: PMC5134364 DOI: 10.3389/fnins.2016.00522] [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: 02/24/2016] [Accepted: 10/28/2016] [Indexed: 12/20/2022] Open
Abstract
Voxel based morphometry (VBM) is a widely utilized neuroimaging technique for spatially normalizing brain structural MRI (sMRI) onto a common template. The DARTEL technique of VBM takes into account the spatial intensity distribution of sMRIs to construct a study specific group template. The group template is then used to create final individual normalized tissue maps (FINTM) for each subject in the group. In this study, we investigate the effect of group on FINTM, i.e., we evaluate the variability of a constant subject's FINTM when other subjects in the group are iteratively changed. We examine this variability under the following scenarios: (1) when the demographics of the iterative groups are similar, (2) when the average age of the iterative groups is increased, and (3) when the number of subjects with a brain disorder (here we use subjects with autism) is increased. Our results show that when subject demographics of the group remains similar the mean standard deviation (SD) of FINTM gray matter (GM) of the constant subject was around 0.01. As the average age of the group is increased, mean SD of GM increased to around 0.03 and at certain brain locations variability was as high as 0.23. A similar increase in variability was observed when the number of autism subjects in the group was increased where mean SD was around 0.02. Further, we find that autism vs. control GM differences are in the range of -0.05 to +0.05 for more than 97% of the voxels and note that the magnitude of the differences are comparable to GM variability. Finally, we report that opting not to modulate during normalization or increasing the size of the smoothing kernel can decrease FINTM variability but at the loss of subject-specific features. Based on the findings of this study, we outline precautions that should be considered by investigators to reduce the impact of group on FINTM and thereby derive more meaningful group differences when comparing two cohorts of subjects.
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Affiliation(s)
- Andrew M Michael
- Autism & Developmental Medicine Institute, Geisinger Health SystemLewisburg, PA, USA; Institute for Advanced Applications, Geisinger Health SystemDanville, PA, USA
| | - Eli Evans
- Autism & Developmental Medicine Institute, Geisinger Health System Lewisburg, PA, USA
| | - Gregory J Moore
- Autism & Developmental Medicine Institute, Geisinger Health SystemLewisburg, PA, USA; Department of Radiology, Geisinger Health SystemDanville, PA, USA
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38
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Ecker C. The neuroanatomy of autism spectrum disorder: An overview of structural neuroimaging findings and their translatability to the clinical setting. AUTISM : THE INTERNATIONAL JOURNAL OF RESEARCH AND PRACTICE 2016; 21:18-28. [DOI: 10.1177/1362361315627136] [Citation(s) in RCA: 88] [Impact Index Per Article: 11.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/12/2023]
Abstract
Autism spectrum disorder is a complex neurodevelopmental disorder, which is accompanied by differences in brain anatomy, functioning and brain connectivity. Due to its neurodevelopmental character, and the large phenotypic heterogeneity among individuals on the autism spectrum, the neurobiology of autism spectrum disorder is inherently difficult to describe. Nevertheless, significant progress has been made in characterizing the neuroanatomical underpinnings of autism spectrum disorder across the human life span, and in identifying the molecular pathways that may be affected in autism spectrum disorder. Moreover, novel methodological frameworks for analyzing neuroimaging data are emerging that make it possible to characterize the neuroanatomy of autism spectrum disorder on the case level, and to stratify individuals based on their individual phenotypic make up. While these approaches are increasingly more often employed in the research setting, their applicability in the clinical setting remains a vision for the future. The aim of the current review is to (1) provide a general overview of recent structural neuroimaging findings examining the neuroanatomy of autism spectrum disorder across the human life span, and in males and females with the condition, (2) highlight potential neuroimaging (bio)markers that may in the future be used for the stratification of autism spectrum disorder individuals into biologically homogeneous subgroups and (3) inform treatment and intervention strategies.
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Retico A, Gori I, Giuliano A, Muratori F, Calderoni S. One-Class Support Vector Machines Identify the Language and Default Mode Regions As Common Patterns of Structural Alterations in Young Children with Autism Spectrum Disorders. Front Neurosci 2016; 10:306. [PMID: 27445675 PMCID: PMC4925658 DOI: 10.3389/fnins.2016.00306] [Citation(s) in RCA: 18] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/01/2016] [Accepted: 06/16/2016] [Indexed: 01/05/2023] Open
Abstract
The identification of reliable brain endophenotypes of autism spectrum disorders (ASD) has been hampered to date by the heterogeneity in the neuroanatomical abnormalities detected in this condition. To handle the complexity of neuroimaging data and to convert brain images in informative biomarkers of pathology, multivariate analysis techniques based on Support Vector Machines (SVM) have been widely used in several disease conditions. They are usually trained to distinguish patients from healthy control subjects by making a binary classification. Here, we propose the use of the One-Class Classification (OCC) or Data Description method that, in contrast to two-class classification, is based on a description of one class of objects only. This approach, by defining a multivariate normative rule on one class of subjects, allows recognizing examples from a different category as outliers. We applied the OCC to 314 regional features extracted from brain structural Magnetic Resonance Imaging (MRI) scans of young children with ASD (21 males and 20 females) and control subjects (20 males and 20 females), matched on age [range: 22-72 months of age; mean = 49 months] and non-verbal intelligence quotient (NVIQ) [range: 31-123; mean = 73]. We demonstrated that a common pattern of features characterize the ASD population. The OCC SVM trained on the group of ASD subjects showed the following performances in the ASD vs. controls separation: the area under the receiver operating characteristic curve (AUC) was 0.74 for the male and 0.68 for the female population, respectively. Notably, the ASD vs. controls discrimination results were maximized when evaluated on the subsamples of subjects with NVIQ ≥ 70, leading to AUC = 0.81 for the male and AUC = 0.72 for the female populations, respectively. Language regions and regions from the default mode network-posterior cingulate cortex, pars opercularis and pars triangularis of the inferior frontal gyrus, and transverse temporal gyrus-contributed most to distinguishing individuals with ASD from controls, arguing for the crucial role of these areas in the ASD pathophysiology. The observed brain patterns associate preschoolers with ASD independently of their age, gender and NVIQ and therefore they are expected to constitute part of the ASD brain endophenotype.
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Affiliation(s)
| | - Ilaria Gori
- Pisa Division, National Institute for Nuclear PhysicsPisa, Italy
- Department of Chemistry and Pharmacy, University of SassariSassari, Italy
| | - Alessia Giuliano
- Pisa Division, National Institute for Nuclear PhysicsPisa, Italy
- Department of Physics, University of PisaPisa, Italy
| | - Filippo Muratori
- Department of Developmental Neuroscience, IRCCS Stella Maris FoundationPisa, Italy
- Department of Clinical and Experimental Medicine, University of PisaPisa, Italy
| | - Sara Calderoni
- Department of Developmental Neuroscience, IRCCS Stella Maris FoundationPisa, Italy
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40
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Katuwal GJ, Baum SA, Cahill ND, Michael AM. Divide and Conquer: Sub-Grouping of ASD Improves ASD Detection Based on Brain Morphometry. PLoS One 2016; 11:e0153331. [PMID: 27065101 PMCID: PMC4827874 DOI: 10.1371/journal.pone.0153331] [Citation(s) in RCA: 31] [Impact Index Per Article: 3.9] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/19/2015] [Accepted: 03/28/2016] [Indexed: 11/30/2022] Open
Abstract
Low success (<60%) in autism spectrum disorder (ASD) classification using brain morphometry from the large multi-site ABIDE dataset and inconsistent findings on brain morphometric abnormalities in ASD can be attributed to the ASD heterogeneity. In this study, we show that ASD brain morphometry is highly heterogeneous, and demonstrate that the heterogeneity can be mitigated and classification improved if autism severity (AS), verbal IQ (VIQ) and age are used with morphometric features. Morphometric features from structural MRIs (sMRIs) of 734 males (ASD: 361, controls: 373) of ABIDE were derived using FreeSurfer. Applying the Random Forest classifier, an AUC of 0.61 was achieved. Adding VIQ and age to morphometric features, AUC improved to 0.68. Sub-grouping the subjects by AS, VIQ and age improved the classification with the highest AUC of 0.8 in the moderate-AS sub-group (AS = 7–8). Matching subjects on age and/or VIQ in each sub-group further improved the classification with the highest AUC of 0.92 in the low AS sub-group (AS = 4–5). AUC decreased with AS and VIQ, and was the lowest in the mid-age sub-group (13–18 years). The important features were mainly from the frontal, temporal, ventricular, right hippocampal and left amygdala regions. However, they highly varied with AS, VIQ and age. The curvature and folding index features from frontal, temporal, lingual and insular regions were dominant in younger subjects suggesting their importance for early detection. When the experiments were repeated using the Gradient Boosting classifier similar results were obtained. Our findings suggest that identifying brain biomarkers in sub-groups of ASD can yield more robust and insightful results than searching across the whole spectrum. Further, it may allow identification of sub-group specific brain biomarkers that are optimized for early detection and monitoring, increasing the utility of sMRI as an important tool for early detection of ASD.
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Affiliation(s)
- Gajendra J. Katuwal
- Autism and Developmental Medicine Institute, Geisinger Health System, Danville, PA, United States of America
- Chester F. Carlson Center for Imaging Science, Rochester Institute of Technology, Rochester, NY, United States of America
- * E-mail:
| | - Stefi A. Baum
- Chester F. Carlson Center for Imaging Science, Rochester Institute of Technology, Rochester, NY, United States of America
- Faculty of Science, University of Manitoba, Winnipeg, Canada
| | - Nathan D. Cahill
- Chester F. Carlson Center for Imaging Science, Rochester Institute of Technology, Rochester, NY, United States of America
- School of Mathematical Sciences, Rochester Institute of Technology, Rochester, NY, United States of America
| | - Andrew M. Michael
- Autism and Developmental Medicine Institute, Geisinger Health System, Danville, PA, United States of America
- Chester F. Carlson Center for Imaging Science, Rochester Institute of Technology, Rochester, NY, United States of America
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Jumah F, Ghannam M, Jaber M, Adeeb N, Tubbs RS. Neuroanatomical variation in autism spectrum disorder: A comprehensive review. Clin Anat 2016; 29:454-65. [PMID: 27004599 DOI: 10.1002/ca.22717] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.1] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/16/2016] [Accepted: 03/18/2016] [Indexed: 01/27/2023]
Abstract
Autism Spectrum Disorder (ASD) is a neurodevelopmental disorder characterized by impairments in socialization, communication, and behavior. Many investigators have described the anatomical abnormalities in autistic brains, in an attempt to correlate them with the manifestations of ASD. Herein, we reviewed all the available literature about the neuroanatomical findings in ASD available via "PubMed" and "Google Scholar." References found in review articles were also searched manually. There was substantial discrepancy throughout the literature regarding the reported presence and significance of neuroanatomical findings in ASD, and this is thoroughly discussed in the present review.
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Affiliation(s)
- Fareed Jumah
- Department of Neuroscience, an-Najah National University Hospital, Nablus, Palestine
| | - Malik Ghannam
- Department of Neuroscience, an-Najah National University Hospital, Nablus, Palestine
| | - Mohammad Jaber
- Department of Neuroscience, an-Najah National University Hospital, Nablus, Palestine
| | - Nimer Adeeb
- Department of Neurosurgery, Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, Massachusetts
| | - R Shane Tubbs
- Department of Anatomical Sciences, St. George's University, Grenada.,Seattle Science Foundation, Seattle, Washington
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42
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Retico A, Giuliano A, Tancredi R, Cosenza A, Apicella F, Narzisi A, Biagi L, Tosetti M, Muratori F, Calderoni S. The effect of gender on the neuroanatomy of children with autism spectrum disorders: a support vector machine case-control study. Mol Autism 2016; 7:5. [PMID: 26788282 PMCID: PMC4717545 DOI: 10.1186/s13229-015-0067-3] [Citation(s) in RCA: 66] [Impact Index Per Article: 8.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/15/2015] [Accepted: 12/30/2015] [Indexed: 01/07/2023] Open
Abstract
Background Genetic, hormonal, and environmental factors contribute since infancy to sexual dimorphism in regional brain structures of subjects with typical development. However, the neuroanatomical differences between male and female children with autism spectrum disorders (ASD) are an intriguing and still poorly investigated issue. This study aims to evaluate whether the brain of young children with ASD exhibits sex-related structural differences and if a correlation exists between clinical ASD features and neuroanatomical underpinnings. Methods A total of 152 structural MRI scans were analysed. Specifically, 76 young children with ASD (38 males and 38 females; 2–7 years of age; mean = 53 months, standard deviation = 17 months) were evaluated employing a support vector machine (SVM)-based analysis of the grey matter (GM). Group comparisons consisted of 76 age-, gender- and non-verbal-intelligence quotient-matched children with typical development or idiopathic developmental delay without autism. Results For both genders combined, SVM showed a significantly increased GM volume in young children with ASD with respect to control subjects, predominantly in the bilateral superior frontal gyrus (Brodmann area –BA– 10), bilateral precuneus (BA 31), bilateral superior temporal gyrus (BA 20/22), whereas less GM in patients with ASD was found in right inferior temporal gyrus (BA 37). For the within gender comparisons (i.e., females with ASD vs. controls and males with ASD vs. controls), two overlapping regions in bilateral precuneus (BA 31) and left superior frontal gyrus (BA 9/10) were detected. Sex-by-group analyses revealed in males with ASD compared to matched controls two male-specific regions of increased GM volume (left middle occipital gyrus—BA 19—and right superior temporal gyrus—BA 22). Comparisons in females with and without ASD demonstrated increased GM volumes predominantly in the bilateral frontal regions. Additional regions of significantly increased GM volume in the right anterior cingulate cortex (BA 32) and right cerebellum were typical only of females with ASD. Conclusions Despite the specific behavioural correlates of sex-dimorphism in ASD, brain morphology as yet remains unclear and requires future dedicated investigations. This study provides evidence of structural brain gender differences in young children with ASD that possibly contribute to the different phenotypic disease manifestations in males and females. Electronic supplementary material The online version of this article (doi:10.1186/s13229-015-0067-3) contains supplementary material, which is available to authorized users.
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Affiliation(s)
- Alessandra Retico
- Istituto Nazionale di Fisica Nucleare, Pisa Division, Largo B. Pontecorvo 3, 56127 Pisa, Italy
| | - Alessia Giuliano
- Istituto Nazionale di Fisica Nucleare, Pisa Division, Largo B. Pontecorvo 3, 56127 Pisa, Italy ; University of Pisa, Department of Physics, Largo B. Pontecorvo 3, 56127 Pisa, Italy
| | | | - Angela Cosenza
- IRCCS Stella Maris Foundation, viale del Tirreno 331, 56018 Pisa, Italy
| | - Fabio Apicella
- IRCCS Stella Maris Foundation, viale del Tirreno 331, 56018 Pisa, Italy
| | - Antonio Narzisi
- IRCCS Stella Maris Foundation, viale del Tirreno 331, 56018 Pisa, Italy
| | - Laura Biagi
- IRCCS Stella Maris Foundation, viale del Tirreno 331, 56018 Pisa, Italy
| | - Michela Tosetti
- IRCCS Stella Maris Foundation, viale del Tirreno 331, 56018 Pisa, Italy
| | - Filippo Muratori
- IRCCS Stella Maris Foundation, viale del Tirreno 331, 56018 Pisa, Italy ; University of Pisa, Department of Clinical and Experimental Medicine, Via Savi 10, 56126 Pisa, Italy
| | - Sara Calderoni
- IRCCS Stella Maris Foundation, viale del Tirreno 331, 56018 Pisa, Italy
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Sacco R, Gabriele S, Persico AM. Head circumference and brain size in autism spectrum disorder: A systematic review and meta-analysis. Psychiatry Res 2015; 234:239-51. [PMID: 26456415 DOI: 10.1016/j.pscychresns.2015.08.016] [Citation(s) in RCA: 131] [Impact Index Per Article: 14.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/19/2015] [Accepted: 08/25/2015] [Indexed: 11/29/2022]
Abstract
Macrocephaly and brain overgrowth have been associated with autism spectrum disorder. We performed a systematic review and meta-analysis to provide an overall estimate of effect size and statistical significance for both head circumference and total brain volume in autism. Our literature search strategy identified 261 and 391 records, respectively; 27 studies defining percentages of macrocephalic patients and 44 structural brain imaging studies providing total brain volumes for patients and controls were included in our meta-analyses. Head circumference was significantly larger in autistic compared to control individuals, with 822/5225 (15.7%) autistic individuals displaying macrocephaly. Structural brain imaging studies measuring brain volume estimated effect size. The effect size is higher in low functioning autistics compared to high functioning and ASD individuals. Brain overgrowth was recorded in 142/1558 (9.1%) autistic patients. Finally, we found a significant interaction between age and total brain volume, resulting in larger head circumference and brain size during early childhood. Our results provide conclusive effect sizes and prevalence rates for macrocephaly and brain overgrowth in autism, confirm the variation of abnormal brain growth with age, and support the inclusion of this endophenotype in multi-biomarker diagnostic panels for clinical use.
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Affiliation(s)
- Roberto Sacco
- Unit of Child and Adolescent NeuroPsychiatry, Laboratory of Molecular Psychiatry and Neurogenetics, University "Campus Bio-Medico", Rome, Italy.
| | - Stefano Gabriele
- Unit of Child and Adolescent NeuroPsychiatry, Laboratory of Molecular Psychiatry and Neurogenetics, University "Campus Bio-Medico", Rome, Italy
| | - Antonio M Persico
- Unit of Child and Adolescent NeuroPsychiatry, Laboratory of Molecular Psychiatry and Neurogenetics, University "Campus Bio-Medico", Rome, Italy; Mafalda Luce Center for Pervasive Developmental Disorders, Milan, Italy
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44
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Ding X, Yang Y, Stein EA, Ross TJ. Multivariate classification of smokers and nonsmokers using SVM-RFE on structural MRI images. Hum Brain Mapp 2015; 36:4869-79. [PMID: 26497657 DOI: 10.1002/hbm.22956] [Citation(s) in RCA: 34] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/31/2014] [Revised: 08/13/2015] [Accepted: 08/17/2015] [Indexed: 11/08/2022] Open
Abstract
Voxel-based morphometry (VBM) studies have revealed gray matter alterations in smokers, but this type of analysis has poor predictive value for individual cases, which limits its applicability in clinical diagnoses and treatment. A predictive model would essentially embody a complex biomarker that could be used to evaluate treatment efficacy. In this study, we applied VBM along with a multivariate classification method consisting of a support vector machine with recursive feature elimination to discriminate smokers from nonsmokers using their structural MRI data. Mean gray matter volumes in 1,024 cerebral cortical regions of interest created using a subparcellated version of the Automated Anatomical Labeling template were calculated from 60 smokers and 60 nonsmokers, and served as input features to the classification procedure. The classifier achieved the highest accuracy of 69.6% when taking the 139 highest ranked features via 10-fold cross-validation. Critically, these features were later validated on an independent testing set that consisted of 28 smokers and 28 nonsmokers, yielding a 64.04% accuracy level (binomial P = 0.01). Following classification, exploratory post hoc regression analyses were performed, which revealed that gray matter volumes in the putamen, hippocampus, prefrontal cortex, cingulate cortex, caudate, thalamus, pre-/postcentral gyrus, precuneus, and the parahippocampal gyrus, were inversely related to smoking behavioral characteristics. These results not only indicate that smoking related gray matter alterations can provide predictive power for group membership, but also suggest that machine learning techniques can reveal underlying smoking-related neurobiology.
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Affiliation(s)
- Xiaoyu Ding
- Neuroimaging Research Branch, Intramural Research Program, National Institute on Drug Abuse, National Institutes of Health, Baltimore, Maryland
| | - Yihong Yang
- Neuroimaging Research Branch, Intramural Research Program, National Institute on Drug Abuse, National Institutes of Health, Baltimore, Maryland
| | - Elliot A Stein
- Neuroimaging Research Branch, Intramural Research Program, National Institute on Drug Abuse, National Institutes of Health, Baltimore, Maryland
| | - Thomas J Ross
- Neuroimaging Research Branch, Intramural Research Program, National Institute on Drug Abuse, National Institutes of Health, Baltimore, Maryland
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45
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Levman J, Takahashi E. Multivariate analyses applied to fetal, neonatal and pediatric MRI of neurodevelopmental disorders. Neuroimage Clin 2015; 9:532-44. [PMID: 26640765 PMCID: PMC4625213 DOI: 10.1016/j.nicl.2015.09.017] [Citation(s) in RCA: 17] [Impact Index Per Article: 1.9] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/24/2015] [Revised: 09/23/2015] [Accepted: 09/25/2015] [Indexed: 01/15/2023]
Abstract
Multivariate analysis (MVA) is a class of statistical and pattern recognition methods that involve the processing of data that contains multiple measurements per sample. MVA can be used to address a wide variety of medical neuroimaging-related challenges including identifying variables associated with a measure of clinical importance (i.e. patient outcome), creating diagnostic tests, assisting in characterizing developmental disorders, understanding disease etiology, development and progression, assisting in treatment monitoring and much more. Compared to adults, imaging of developing immature brains has attracted less attention from MVA researchers. However, remarkable MVA research growth has occurred in recent years. This paper presents the results of a systematic review of the literature focusing on MVA technologies applied to neurodevelopmental disorders in fetal, neonatal and pediatric magnetic resonance imaging (MRI) of the brain. The goal of this manuscript is to provide a concise review of the state of the scientific literature on studies employing brain MRI and MVA in a pre-adult population. Neurological developmental disorders addressed in the MVA research contained in this review include autism spectrum disorder, attention deficit hyperactivity disorder, epilepsy, schizophrenia and more. While the results of this review demonstrate considerable interest from the scientific community in applications of MVA technologies in pediatric/neonatal/fetal brain MRI, the field is still young and considerable research growth remains ahead of us.
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Affiliation(s)
- Jacob Levman
- Division of Newborn Medicine, Department of Medicine, Boston Children's Hospital, Harvard Medical School, 1 Autumn Street #456, Boston, MA 02115, USA
- Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, 149 13th Street, Charlestown, MA 02129, USA
| | - Emi Takahashi
- Division of Newborn Medicine, Department of Medicine, Boston Children's Hospital, Harvard Medical School, 1 Autumn Street #456, Boston, MA 02115, USA
- Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, 149 13th Street, Charlestown, MA 02129, USA
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46
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Supekar K, Menon V. Sex differences in structural organization of motor systems and their dissociable links with repetitive/restricted behaviors in children with autism. Mol Autism 2015; 6:50. [PMID: 26347127 PMCID: PMC4559968 DOI: 10.1186/s13229-015-0042-z] [Citation(s) in RCA: 76] [Impact Index Per Article: 8.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/08/2015] [Accepted: 08/17/2015] [Indexed: 12/03/2022] Open
Abstract
Background Autism spectrum disorder (ASD) is diagnosed much less often in females than males. Emerging behavioral accounts suggest that the clinical presentation of autism is different in females and males, yet research examining sex differences in core symptoms of autism in affected children has been limited. Additionally, to date, there have been no systematic attempts to characterize neuroanatomical differences underlying the distinct behavioral profiles observed in girls and boys with ASD. This is in part because extant ASD studies have included a small number of girls. Methods Leveraging the National Database for Autism Research (NDAR), we first analyzed symptom severity in a large sample consisting of 128 ASD girls and 614 age- and IQ-matched ASD boys. We then examined symptom severity and structural imaging data using novel multivariate pattern analysis in a well-matched group of 25 ASD girls, 25 ASD boys, 19 typically developing (TD) girls, and 19 TD boys, obtained from the Autism Brain Imaging Data Exchange (ABIDE). Results In both the NDAR and ABIDE datasets, girls, compared to boys, with ASD showed less severe repetitive/restricted behaviors (RRBs) and comparable deficits in the social and communication domains. In the ABIDE imaging dataset, gray matter (GM) patterns in the motor cortex, supplementary motor area (SMA), cerebellum, fusiform gyrus, and amygdala accurately discriminated girls and boys with ASD. This sex difference pattern was specific to ASD as the GM in these brain regions did not discriminate TD girls and boys. Moreover, GM in the motor cortex, SMA, and crus 1 subdivision of the cerebellum was correlated with RRB in girls whereas GM in the right putamen—the region that discriminated TD girls and boys—was correlated with RRB in boys. Conclusions We found robust evidence for reduced levels of RRB in girls, compared to boys, with ASD, providing the strongest evidence to date for sex differences in a core phenotypic feature of childhood ASD. Sex differences in brain morphometry are prominent in the motor system and in areas that comprise the “social brain.” Notably, RRB severity is associated with sex differences in GM morphometry in distinct motor regions. Our findings provide novel insights into the neurobiology of sex differences in childhood autism. Electronic supplementary material The online version of this article (doi:10.1186/s13229-015-0042-z) contains supplementary material, which is available to authorized users.
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Affiliation(s)
- Kaustubh Supekar
- Department of Psychiatry & Behavioral Sciences, Stanford University School of Medicine, 401 Quarry Rd., Stanford, CA 94304-5719 USA
| | - Vinod Menon
- Department of Psychiatry & Behavioral Sciences, Stanford University School of Medicine, 401 Quarry Rd., Stanford, CA 94304-5719 USA ; Department of Neurology and Neurological Sciences, Stanford University School of Medicine, Stanford, CA 94304 USA ; Stanford Neurosciences Institute, Stanford University School of Medicine, Stanford, CA 94304 USA
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47
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Gori I, Giuliano A, Muratori F, Saviozzi I, Oliva P, Tancredi R, Cosenza A, Tosetti M, Calderoni S, Retico A. Gray Matter Alterations in Young Children with Autism Spectrum Disorders: Comparing Morphometry at the Voxel and Regional Level. J Neuroimaging 2015. [PMID: 26214066 DOI: 10.1111/jon.12280] [Citation(s) in RCA: 43] [Impact Index Per Article: 4.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/22/2022] Open
Abstract
BACKGROUND AND PURPOSE Sophisticated algorithms to infer disease diagnosis, pathology progression and patient outcome are increasingly being developed to analyze brain MRI data. They have been successfully implemented in a variety of diseases and are currently investigated in the field of neuropsychiatric disorders, including autism spectrum disorder (ASD). We aim to test the ability to predict ASD from subtle morphological changes in structural magnetic resonance imaging (sMRI). METHODS The analysis of sMRI of a cohort of male ASD children and controls matched for age and nonverbal intelligence quotient (NVIQ) has been carried out with two widely used preprocessing software packages (SPM and Freesurfer) to extract brain morphometric information at different spatial scales. Then, support vector machines have been implemented to classify the brain features and to localize which brain regions contribute most to the ASD-control separation. RESULTS The features extracted from the gray matter subregions provide the best classification performance, reaching an area under the receiver operating characteristic curve (AUC) of 74%. This value is enhanced to 80% when considering only subjects with NVIQ over 70. CONCLUSIONS Despite the subtle impact of ASD on brain morphology and a limited cohort size, results from sMRI-based classifiers suggest a consistent network of altered brain regions.
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Affiliation(s)
- Ilaria Gori
- Istituto Nazionale di Fisica Nucleare, Sezione di Pisa, Italy.,Dipartimento di Chimica e Farmacia, Università di Sassari, Italy
| | - Alessia Giuliano
- Istituto Nazionale di Fisica Nucleare, Sezione di Pisa, Italy.,Dipartimento di Chimica e Farmacia, Università di Sassari, Italy.,Dipartimento di Fisica, Università di Pisa, Italy
| | - Filippo Muratori
- IRCCS Fondazione Stella Maris, Pisa, Italy.,Dipartimento di Medicina Clinica e Sperimentale, Università of Pisa, Italy
| | | | - Piernicola Oliva
- Dipartimento di Chimica e Farmacia, Università di Sassari, Italy.,Istituto Nazionale di Fisica Nucleare, Sezione di Cagliari, Italy
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48
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Neuroimaging in autism spectrum disorder: brain structure and function across the lifespan. Lancet Neurol 2015; 14:1121-34. [PMID: 25891007 DOI: 10.1016/s1474-4422(15)00050-2] [Citation(s) in RCA: 266] [Impact Index Per Article: 29.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/11/2015] [Revised: 03/25/2015] [Accepted: 04/13/2015] [Indexed: 12/22/2022]
Abstract
Over the past decade, in-vivo MRI studies have provided many invaluable insights into the neural substrates underlying autism spectrum disorder (ASD), which is now known to be associated with neurodevelopmental variations in brain anatomy, functioning, and connectivity. These systems-level features of ASD pathology seem to develop differentially across the human lifespan so that the cortical abnormalities that occur in children with ASD differ from those noted at other stages of life. Thus, investigation of the brain in ASD poses particular methodological challenges, which must be addressed to enable the comparison of results across studies. Novel analytical approaches are also being developed to facilitate the translation of findings from the research to the clinical setting. In the future, the insights provided by human neuroimaging studies could contribute to biomarker development for ASD and other neurodevelopmental disorders, and to new approaches to diagnosis and treatment.
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49
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Conti E, Calderoni S, Marchi V, Muratori F, Cioni G, Guzzetta A. The first 1000 days of the autistic brain: a systematic review of diffusion imaging studies. Front Hum Neurosci 2015; 9:159. [PMID: 25859207 PMCID: PMC4374458 DOI: 10.3389/fnhum.2015.00159] [Citation(s) in RCA: 38] [Impact Index Per Article: 4.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/15/2014] [Accepted: 03/07/2015] [Indexed: 02/03/2023] Open
Abstract
There is overwhelming evidence that autism spectrum disorder (ASD) is related to altered brain connectivity. While these alterations are starting to be well characterized in subjects where the clinical picture is fully expressed, less is known on their earlier developmental course. In the present study we systematically reviewed current knowledge on structural connectivity in ASD infants and toddlers. We searched PubMed and Medline databases for all English language papers, published from year 2000, exploring structural connectivity in populations of infants and toddlers whose mean age was below 30 months. Of the 264 papers extracted, four were found to be eligible and were reviewed. Three of the four selected studies reported higher fractional anisotropy values in subjects with ASD compared to controls within commissural fibers, projections fibers, and association fibers, suggesting brain hyper-connectivity in the earliest phases of the disorder. Similar conclusions emerged from the other diffusion parameters assessed. These findings are reversed to what is generally found in studies exploring older patient groups and suggest a developmental course characterized by a shift toward hypo-connectivity starting at a time between two and four years of age.
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Affiliation(s)
- Eugenia Conti
- Department of Developmental Neuroscience, Stella Maris Scientific Institute, IRCCS Stella Maris Foundation Pisa, Italy ; Department of Clinical and Experimental Medicine, University of Pisa Pisa, Italy
| | - Sara Calderoni
- Department of Developmental Neuroscience, Stella Maris Scientific Institute, IRCCS Stella Maris Foundation Pisa, Italy
| | - Viviana Marchi
- Department of Developmental Neuroscience, Stella Maris Scientific Institute, IRCCS Stella Maris Foundation Pisa, Italy ; Department of Clinical and Experimental Medicine, University of Pisa Pisa, Italy
| | - Filippo Muratori
- Department of Developmental Neuroscience, Stella Maris Scientific Institute, IRCCS Stella Maris Foundation Pisa, Italy ; Department of Clinical and Experimental Medicine, University of Pisa Pisa, Italy
| | - Giovanni Cioni
- Department of Developmental Neuroscience, Stella Maris Scientific Institute, IRCCS Stella Maris Foundation Pisa, Italy ; Department of Clinical and Experimental Medicine, University of Pisa Pisa, Italy
| | - Andrea Guzzetta
- Department of Developmental Neuroscience, Stella Maris Scientific Institute, IRCCS Stella Maris Foundation Pisa, Italy ; Department of Clinical and Experimental Medicine, University of Pisa Pisa, Italy
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50
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Abstract
Machine learning techniques are increasingly being used in making relevant predictions and inferences on individual subjects neuroimaging scan data. Previous studies have mostly focused on categorical discrimination of patients and matched healthy controls and more recently, on prediction of individual continuous variables such as clinical scores or age. However, these studies are greatly hampered by the large number of predictor variables (voxels) and low observations (subjects) also known as the curse-of-dimensionality or small-n-large-p problem. As a result, feature reduction techniques such as feature subset selection and dimensionality reduction are used to remove redundant predictor variables and experimental noise, a process which mitigates the curse-of-dimensionality and small-n-large-p effects. Feature reduction is an essential step before training a machine learning model to avoid overfitting and therefore improving model prediction accuracy and generalization ability. In this review, we discuss feature reduction techniques used with machine learning in neuroimaging studies.
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Affiliation(s)
- Benson Mwangi
- UT Center of Excellence on Mood Disorders, Department of Psychiatry and Behavioral Sciences, UT Houston Medical School, Houston, TX, 77054, USA,
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