1
|
Wang XH, Wu P, Li L. Predicting individual autistic symptoms for patients with autism spectrum disorder using interregional morphological connectivity. Psychiatry Res Neuroimaging 2024; 341:111822. [PMID: 38678667 DOI: 10.1016/j.pscychresns.2024.111822] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/23/2022] [Revised: 03/28/2024] [Accepted: 04/17/2024] [Indexed: 05/01/2024]
Abstract
Intelligent predictive models for autistic symptoms based on neuroimaging datasets were beneficial for the precise intervention of patients with ASD. The goals of this study were twofold: investigating predictive models for autistic symptoms and discovering the brain connectivity patterns for ASD-related behaviors. To achieve these goals, we obtained a cohort of patients with ASD from the ABIDE project. The autistic symptoms were measured using the Autism Diagnostic Observation Schedule (ADOS). The anatomical MRI datasets were preprocessed using the Freesurfer package, resulting in regional morphological features. For each individual, the interregional morphological network was constructed using a novel feature distance-based method. The predictive models for autistic symptoms were built using the support vector regression (SVR) algorithm with feature selection method. The predicted autistic symptoms (i.e., ADOS social score, ADOS behavior) were significantly correlated to the original measures. The most predictive features for ADOS social scores were located in the bilateral fusiform. The most predictive features for ADOS behavior scores were located in the temporal pole and the lingual gyrus. In summary, the autistic symptoms could be predicted using the interregional morphological connectivity and machine learning. The interregional morphological connectivity could be a potential biomarker for autistic symptoms.
Collapse
Affiliation(s)
- Xun-Heng Wang
- Institute of Biomedical Engineering and Instrumentation, Hangzhou Dianzi University, Hangzhou, 310018, China.
| | - Peng Wu
- Institute of Biomedical Engineering and Instrumentation, Hangzhou Dianzi University, Hangzhou, 310018, China
| | - Lihua Li
- Institute of Biomedical Engineering and Instrumentation, Hangzhou Dianzi University, Hangzhou, 310018, China
| |
Collapse
|
2
|
Blasco-Fontecilla H, Li C, Vizcaino M, Fernández-Fernández R, Royuela A, Bella-Fernández M. A Nomogram for Predicting ADHD and ASD in Child and Adolescent Mental Health Services (CAMHS). J Clin Med 2024; 13:2397. [PMID: 38673670 PMCID: PMC11051553 DOI: 10.3390/jcm13082397] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/05/2024] [Revised: 04/08/2024] [Accepted: 04/13/2024] [Indexed: 04/28/2024] Open
Abstract
Objectives: To enhance the early detection of Attention Deficit/Hyperactivity Disorder (ADHD) and Autism Spectrum Disorder (ASD) by leveraging clinical variables collected at child and adolescent mental health services (CAMHS). Methods: This study included children diagnosed with ADHD and/or ASD (n = 857). Three logistic regression models were developed to predict the presence of ADHD, its subtypes, and ASD. The analysis began with univariate logistic regression, followed by a multicollinearity diagnostic. A backward logistic regression selection strategy was then employed to retain variables with p < 0.05. Ethical approval was obtained from the local ethics committee. The models' internal validity was evaluated based on their calibration and discriminative abilities. Results: The study produced models that are well-calibrated and validated for predicting ADHD (incorporating variables such as physical activity, history of bone fractures, and admissions to pediatric/psychiatric services) and ASD (including disability, gender, special education needs, and Axis V diagnoses, among others). Conclusions: Clinical variables can play a significant role in enhancing the early identification of ADHD and ASD.
Collapse
Affiliation(s)
- Hilario Blasco-Fontecilla
- Instituto de Investigación, Transferencia e Innovación, Ciencias de la Saludy Escuela de Doctorado, Universidad Internacional de La Rioja, 26006 Logroño, Spain
- Center of Biomedical Network Research on Mental Health (CIBERSAM), Carlos III Institute of Health, 28029 Madrid, Spain
| | - Chao Li
- Faculty of Medicine, Universidad Autónoma de Madrid, 28049 Madrid, Spain;
| | | | | | - Ana Royuela
- Biostatistics Unit, Hospital Universitario Puerta de Hierro Majadahonda, 28222 Majadahonda, Spain;
| | - Marcos Bella-Fernández
- Puerta de Hierro University Hospital, 28222 Majadahonda, Spain;
- Faculty of Psychology, Universidad Autónoma de Madrid, 28049 Madrid, Spain
| |
Collapse
|
3
|
Siqueiros-Sanchez M, Rai B, Chowdhury S, Reiss AL, Green T. Syndrome-Specific Neuroanatomical Phenotypes in Girls With Turner and Noonan Syndromes. BIOLOGICAL PSYCHIATRY. COGNITIVE NEUROSCIENCE AND NEUROIMAGING 2024; 9:146-155. [PMID: 36084900 PMCID: PMC10305746 DOI: 10.1016/j.bpsc.2022.08.012] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/11/2022] [Revised: 07/20/2022] [Accepted: 08/25/2022] [Indexed: 06/03/2023]
Abstract
BACKGROUND Turner syndrome (TS) and Noonan syndrome (NS) are distinct genetic conditions with highly similar physical and neurodevelopmental phenotypes. TS is caused by X chromosome absence, whereas NS results from genetic mutations activating the Ras-mitogen-activated protein kinase signaling pathway. Previous neuroimaging studies in individuals with TS and NS have shown neuroanatomical variations relative to typically developing individuals, a standard comparison group when initially examining a clinical group of interest. However, none of these studies included a second clinical comparison group, limiting their ability to identify syndrome-specific neuroanatomical phenotypes. METHODS In this study, we compared the behavioral and brain phenotypes of 37 girls with TS, 26 girls with NS, and 37 typically developing girls, all ages 5 to 12 years, using univariate and multivariate data-driven analyses. RESULTS We found divergent neuroanatomical phenotypes between groups, despite high behavioral similarities. Relative to the typically developing group, TS was associated with smaller whole-brain cortical surface area (p ≤ .0001), whereas NS was associated with smaller whole-brain cortical thickness (p = .013). TS was associated with larger subcortical volumes (left amygdala, p = .002; right hippocampus, p = .002), whereas NS was associated with smaller subcortical volumes (bilateral caudate, p ≤ .003; putamen, p < .001; pallidum, p < .001; right hippocampus, p = .015). Multivariate analyses also showed diverging brain phenotypes in terms of surface area and cortical thickness, with surface area outperforming cortical thickness at group separation. CONCLUSIONS TS and NS have syndrome-specific brain phenotypes, despite their behavioral similarities. Our observations suggest that neuroanatomical phenotypes better reflect the different genetic etiologies of TS and NS and may be superior biomarkers relative to behavioral phenotypes.
Collapse
Affiliation(s)
- Monica Siqueiros-Sanchez
- Brain Imaging, Development and Genetics Lab, Department of Psychiatry and Behavioral Sciences, Stanford University School of Medicine, Stanford, California; Division of Interdisciplinary Brain Sciences, Department of Psychiatry and Behavioral Sciences, Stanford University School of Medicine, Stanford, California
| | - Bhavana Rai
- Brain Imaging, Development and Genetics Lab, Department of Psychiatry and Behavioral Sciences, Stanford University School of Medicine, Stanford, California; Division of Interdisciplinary Brain Sciences, Department of Psychiatry and Behavioral Sciences, Stanford University School of Medicine, Stanford, California
| | - Samir Chowdhury
- Division of Interdisciplinary Brain Sciences, Department of Psychiatry and Behavioral Sciences, Stanford University School of Medicine, Stanford, California; Brain Dynamics Lab, Department of Psychiatry and Behavioral Sciences, Stanford University School of Medicine, Stanford, California
| | - Allan L Reiss
- Division of Interdisciplinary Brain Sciences, Department of Psychiatry and Behavioral Sciences, Stanford University School of Medicine, Stanford, California; Department of Radiology, Stanford University School of Medicine, Stanford University, Stanford, California; Department Pediatrics, Stanford University School of Medicine, Stanford University, Stanford, California
| | - Tamar Green
- Brain Imaging, Development and Genetics Lab, Department of Psychiatry and Behavioral Sciences, Stanford University School of Medicine, Stanford, California; Division of Interdisciplinary Brain Sciences, Department of Psychiatry and Behavioral Sciences, Stanford University School of Medicine, Stanford, California.
| |
Collapse
|
4
|
Gao J, Xu Y, Li Y, Lu F, Wang Z. Comprehensive exploration of multi-modal and multi-branch imaging markers for autism diagnosis and interpretation: insights from an advanced deep learning model. Cereb Cortex 2024; 34:bhad521. [PMID: 38220572 DOI: 10.1093/cercor/bhad521] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/31/2023] [Revised: 12/11/2023] [Accepted: 12/12/2023] [Indexed: 01/16/2024] Open
Abstract
Autism spectrum disorder is a complex neurodevelopmental condition with diverse genetic and brain involvement. Despite magnetic resonance imaging advances, autism spectrum disorder diagnosis and understanding its neurogenetic factors remain challenging. We propose a dual-branch graph neural network that effectively extracts and fuses features from bimodalities, achieving 73.9% diagnostic accuracy. To explain the mechanism distinguishing autism spectrum disorder from healthy controls, we establish a perturbation model for brain imaging markers and perform a neuro-transcriptomic joint analysis using partial least squares regression and enrichment to identify potential genetic biomarkers. The perturbation model identifies brain imaging markers related to structural magnetic resonance imaging in the frontal, temporal, parietal, and occipital lobes, while functional magnetic resonance imaging markers primarily reside in the frontal, temporal, occipital lobes, and cerebellum. The neuro-transcriptomic joint analysis highlights genes associated with biological processes, such as "presynapse," "behavior," and "modulation of chemical synaptic transmission" in autism spectrum disorder's brain development. Different magnetic resonance imaging modalities offer complementary information for autism spectrum disorder diagnosis. Our dual-branch graph neural network achieves high accuracy and identifies abnormal brain regions and the neuro-transcriptomic analysis uncovers important genetic biomarkers. Overall, our study presents an effective approach for assisting in autism spectrum disorder diagnosis and identifying genetic biomarkers, showing potential for enhancing the diagnosis and treatment of this condition.
Collapse
Affiliation(s)
- Jingjing Gao
- School of Information and Communication Engineering, University of Electronic Science and Technology of China, Chengdu 611731, China
| | - Yuhang Xu
- School of Information and Communication Engineering, University of Electronic Science and Technology of China, Chengdu 611731, China
| | - Yanling Li
- School of Electrical Engineering and Electronic Information, Xihua University, Chengdu 610039, China
| | - Fengmei Lu
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Lab for Neuroinformation, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu 611731, China
| | - Zhengning Wang
- School of Information and Communication Engineering, University of Electronic Science and Technology of China, Chengdu 611731, China
| |
Collapse
|
5
|
Faraji R, Ganji Z, Khandan Khadem Z, Akbari-Lalimi H, Eidy F, Zare H. Volume-based and Surface-Based Methods in Autism Compared with Healthy Controls Are Free surfer and CAT12 in Agreement? IRANIAN JOURNAL OF CHILD NEUROLOGY 2024; 18:93-118. [PMID: 38375127 PMCID: PMC10874516 DOI: 10.22037/ijcn.v18i1.43294] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Subscribe] [Scholar Register] [Received: 09/20/2023] [Accepted: 12/07/2023] [Indexed: 02/21/2024]
Abstract
Objectives Autism Spectrum Disorder (ASD) encompasses a range of neurodevelopmental disorders, and early detection is crucial. This study aims to identify the Regions of Interest (ROIs) with significant differences between healthy controls and individuals with autism, as well as evaluate the agreement between FreeSurfer 6 (FS6) and Computational Anatomy Toolbox (CAT12) methods. Materials & Methods Surface-based and volume-based features were extracted from FS software and CAT12 toolbox for Statistical Parametric Mapping (SPM) software to estimate ROI-wise biomarkers. These biomarkers were compared between 18 males Typically Developing Controls (TDCs) and 40 male subjects with ASD to assess group differences for each method. Finally, agreement and regression analyses were performed between the two methods for TDCs and ASD groups. Results Both methods revealed ROIs with significant differences for each parameter. The Analysis of Covariance (ANCOVA) showed that both TDCs and ASD groups indicated a significant relationship between the two methods (p<0.001). The R2 values for TDCs and ASD groups were 0.692 and 0.680, respectively, demonstrating a moderate correlation between CAT12 and FS6. Bland-Altman graphs showed a moderate level of agreement between the two methods. Conclusion The moderate correlation and agreement between CAT12 and FS6 suggest that while some consistency is observed in the results, CAT12 is not a superior substitute for FS6 software. Further research is needed to identify a potential replacement for this method.
Collapse
Affiliation(s)
- Reyhane Faraji
- Medical Physics Research Center, Mashhad University of Medical Sciences, Mashhad, Iran
| | - Zohreh Ganji
- Medical Physics Research Center, Mashhad University of Medical Sciences, Mashhad, Iran
| | - Zahra Khandan Khadem
- Medical Physics Research Center, Mashhad University of Medical Sciences, Mashhad, Iran
| | - Hossein Akbari-Lalimi
- Medical Physics Research Center, Mashhad University of Medical Sciences, Mashhad, Iran
| | - Fereshteh Eidy
- Department of Biostatistics, Faculty of Medical Sciences, Tarbiat Modares University, Tehran, Iran
| | - Hoda Zare
- Medical Physics Research Center, Mashhad University of Medical Sciences, Mashhad, Iran
- Department of Medical Physics, Faculty of Medicine, Mashhad University of Medical Sciences, Mashhad, Iran
| |
Collapse
|
6
|
Liloia D, Cauda F, Uddin LQ, Manuello J, Mancuso L, Keller R, Nani A, Costa T. Revealing the Selectivity of Neuroanatomical Alteration in Autism Spectrum Disorder via Reverse Inference. BIOLOGICAL PSYCHIATRY. COGNITIVE NEUROSCIENCE AND NEUROIMAGING 2023; 8:1075-1083. [PMID: 35131520 DOI: 10.1016/j.bpsc.2022.01.007] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/06/2021] [Revised: 12/30/2021] [Accepted: 01/24/2022] [Indexed: 06/14/2023]
Abstract
BACKGROUND Although neuroimaging research has identified atypical neuroanatomical substrates in individuals with autism spectrum disorder (ASD), it is at present unclear whether and to what extent disorder-selective gray matter alterations occur in this spectrum of conditions. In fact, a growing body of evidence shows a substantial overlap between the pathomorphological changes across different brain diseases, which may complicate identification of reliable neural markers and differentiation of the anatomical substrates of distinct psychopathologies. METHODS Using a novel data-driven and Bayesian methodology with published voxel-based morphometry data (849 peer-reviewed experiments and 22,304 clinical subjects), this study performs the first reverse inference investigation to explore the selective structural brain alteration profile of ASD. RESULTS We found that specific brain areas exhibit a >90% probability of gray matter alteration selectivity for ASD: the bilateral precuneus (Brodmann area 7), right inferior occipital gyrus (Brodmann area 18), left cerebellar lobule IX and Crus II, right cerebellar lobule VIIIA, and right Crus I. Of note, many brain voxels that are selective for ASD include areas that are posterior components of the default mode network. CONCLUSIONS The identification of these spatial gray matter alteration patterns offers new insights into understanding the complex neurobiological underpinnings of ASD and opens attractive prospects for future neuroimaging-based interventions.
Collapse
Affiliation(s)
- Donato Liloia
- GCS-fMRI Research Group, Koelliker Hospital, and Department of Psychology, University of Turin, Turin, Italy; Functional Neuroimaging and Complex Neural Systems Laboratory, Department of Psychology, University of Turin, Turin, Italy
| | - Franco Cauda
- GCS-fMRI Research Group, Koelliker Hospital, and Department of Psychology, University of Turin, Turin, Italy; Functional Neuroimaging and Complex Neural Systems Laboratory, Department of Psychology, University of Turin, Turin, Italy; Neuroscience Institute of Turin, Turin, Italy
| | - Lucina Q Uddin
- Department of Psychiatry and Biobehavioral Sciences, University of California, Los Angeles, Los Angeles, California
| | - Jordi Manuello
- GCS-fMRI Research Group, Koelliker Hospital, and Department of Psychology, University of Turin, Turin, Italy; Functional Neuroimaging and Complex Neural Systems Laboratory, Department of Psychology, University of Turin, Turin, Italy.
| | - Lorenzo Mancuso
- GCS-fMRI Research Group, Koelliker Hospital, and Department of Psychology, University of Turin, Turin, Italy; Functional Neuroimaging and Complex Neural Systems Laboratory, Department of Psychology, University of Turin, Turin, Italy
| | - Roberto Keller
- Adult Autism Center, DSM Local Health Unit, ASL TO, Turin, Italy
| | - Andrea Nani
- GCS-fMRI Research Group, Koelliker Hospital, and Department of Psychology, University of Turin, Turin, Italy; Functional Neuroimaging and Complex Neural Systems Laboratory, Department of Psychology, University of Turin, Turin, Italy
| | - Tommaso Costa
- GCS-fMRI Research Group, Koelliker Hospital, and Department of Psychology, University of Turin, Turin, Italy; Functional Neuroimaging and Complex Neural Systems Laboratory, Department of Psychology, University of Turin, Turin, Italy; Neuroscience Institute of Turin, Turin, Italy
| |
Collapse
|
7
|
Chen Z, Hu B, Liu X, Becker B, Eickhoff SB, Miao K, Gu X, Tang Y, Dai X, Li C, Leonov A, Xiao Z, Feng Z, Chen J, Chuan-Peng H. Sampling inequalities affect generalization of neuroimaging-based diagnostic classifiers in psychiatry. BMC Med 2023; 21:241. [PMID: 37400814 DOI: 10.1186/s12916-023-02941-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/10/2023] [Accepted: 06/13/2023] [Indexed: 07/05/2023] Open
Abstract
BACKGROUND The development of machine learning models for aiding in the diagnosis of mental disorder is recognized as a significant breakthrough in the field of psychiatry. However, clinical practice of such models remains a challenge, with poor generalizability being a major limitation. METHODS Here, we conducted a pre-registered meta-research assessment on neuroimaging-based models in the psychiatric literature, quantitatively examining global and regional sampling issues over recent decades, from a view that has been relatively underexplored. A total of 476 studies (n = 118,137) were included in the current assessment. Based on these findings, we built a comprehensive 5-star rating system to quantitatively evaluate the quality of existing machine learning models for psychiatric diagnoses. RESULTS A global sampling inequality in these models was revealed quantitatively (sampling Gini coefficient (G) = 0.81, p < .01), varying across different countries (regions) (e.g., China, G = 0.47; the USA, G = 0.58; Germany, G = 0.78; the UK, G = 0.87). Furthermore, the severity of this sampling inequality was significantly predicted by national economic levels (β = - 2.75, p < .001, R2adj = 0.40; r = - .84, 95% CI: - .41 to - .97), and was plausibly predictable for model performance, with higher sampling inequality for reporting higher classification accuracy. Further analyses showed that lack of independent testing (84.24% of models, 95% CI: 81.0-87.5%), improper cross-validation (51.68% of models, 95% CI: 47.2-56.2%), and poor technical transparency (87.8% of models, 95% CI: 84.9-90.8%)/availability (80.88% of models, 95% CI: 77.3-84.4%) are prevailing in current diagnostic classifiers despite improvements over time. Relating to these observations, model performances were found decreased in studies with independent cross-country sampling validations (all p < .001, BF10 > 15). In light of this, we proposed a purpose-built quantitative assessment checklist, which demonstrated that the overall ratings of these models increased by publication year but were negatively associated with model performance. CONCLUSIONS Together, improving sampling economic equality and hence the quality of machine learning models may be a crucial facet to plausibly translating neuroimaging-based diagnostic classifiers into clinical practice.
Collapse
Affiliation(s)
- Zhiyi Chen
- Experimental Research Center for Medical and Psychological Science (ERC-MPS), School of Psychology, Third Military Medical University, Chongqing, China.
- Faculty of Psychology, Southwest University, Chongqing, China.
| | - Bowen Hu
- Faculty of Psychology, Southwest University, Chongqing, China
| | - Xuerong Liu
- Experimental Research Center for Medical and Psychological Science (ERC-MPS), School of Psychology, Third Military Medical University, Chongqing, China
| | - Benjamin Becker
- The Center of Psychosomatic Medicine, Sichuan Provincial Center for Mental Health, Sichuan Provincial People's Hospital, Chengdu, China
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Laboratory for Neuroinformation, University of Electronic Science and Technology of China, Chengdu, China
| | - Simon B Eickhoff
- Institute of Systems Neuroscience, Heinrich Heine University Düsseldorf, Düsseldorf, Germany
| | - Kuan Miao
- Experimental Research Center for Medical and Psychological Science (ERC-MPS), School of Psychology, Third Military Medical University, Chongqing, China
| | - Xingmei Gu
- Experimental Research Center for Medical and Psychological Science (ERC-MPS), School of Psychology, Third Military Medical University, Chongqing, China
| | - Yancheng Tang
- School of Business and Management, Shanghai International Studies University, Shanghai, China
| | - Xin Dai
- Faculty of Psychology, Southwest University, Chongqing, China
| | - Chao Li
- Department of Radiology, The Third Affiliated Hospital, Sun Yat-Sen University, Guangdong, China
| | - Artemiy Leonov
- School of Psychology, Clark University, Worcester, MA, USA
| | - Zhibing Xiao
- State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing, China
| | - Zhengzhi Feng
- Experimental Research Center for Medical and Psychological Science (ERC-MPS), School of Psychology, Third Military Medical University, Chongqing, China
| | - Ji Chen
- Department of Psychology and Behavioral Sciences, Zhejiang University, Hangzhou, China.
- Department of Psychiatry, The Fourth Affiliated Hospital, Zhejiang University School of Medicine, Yiwu, Zhejiang, China.
| | - Hu Chuan-Peng
- School of Psychology, Nanjing Normal University, Nanjing, China
| |
Collapse
|
8
|
Sullivan M, Fernandez-Aranda F, Camacho-Barcia L, Harkin A, Macrì S, Mora-Maltas B, Jiménez-Murcia S, O'Leary A, Ottomana AM, Presta M, Slattery D, Scholtz S, Glennon JC. Insulin and Disorders of Behavioural Flexibility. Neurosci Biobehav Rev 2023; 150:105169. [PMID: 37059405 DOI: 10.1016/j.neubiorev.2023.105169] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/30/2022] [Revised: 04/03/2023] [Accepted: 04/10/2023] [Indexed: 04/16/2023]
Abstract
Behavioural inflexibility is a symptom of neuropsychiatric and neurodegenerative disorders such as Obsessive-Compulsive Disorder, Autism Spectrum Disorder and Alzheimer's Disease, encompassing the maintenance of a behaviour even when no longer appropriate. Recent evidence suggests that insulin signalling has roles apart from its regulation of peripheral metabolism and mediates behaviourally-relevant central nervous system (CNS) functions including behavioural flexibility. Indeed, insulin resistance is reported to generate anxious, perseverative phenotypes in animal models, with the Type 2 diabetes medication metformin proving to be beneficial for disorders including Alzheimer's Disease. Structural and functional neuroimaging studies of Type 2 diabetes patients have highlighted aberrant connectivity in regions governing salience detection, attention, inhibition and memory. As currently available therapeutic strategies feature high rates of resistance, there is an urgent need to better understand the complex aetiology of behaviour and develop improved therapeutics. In this review, we explore the circuitry underlying behavioural flexibility, changes in Type 2 diabetes, the role of insulin in CNS outcomes and mechanisms of insulin involvement across disorders of behavioural inflexibility.
Collapse
Affiliation(s)
- Mairéad Sullivan
- Conway Institute of Biomedical and Biomolecular Research, School of Medicine, University College Dublin, Dublin, Ireland.
| | - Fernando Fernandez-Aranda
- Department of Psychiatry, University Hospital of Bellvitge, Barcelona, Spain; Psychoneurobiology of Eating and Addictive Behaviors Group, Neurosciences Program, Bellvitge Biomedical Research Institute (IDIBELL), Barcelona, Spain; CIBER Fisiopatología Obesidad y Nutrición (CIBERobn), Instituto de Salud Carlos III, Barcelona, Spain; Department of Clinical Sciences, School of Medicine and Health Sciences, University of Barcelona, Barcelona, Spain
| | - Lucía Camacho-Barcia
- Department of Psychiatry, University Hospital of Bellvitge, Barcelona, Spain; Psychoneurobiology of Eating and Addictive Behaviors Group, Neurosciences Program, Bellvitge Biomedical Research Institute (IDIBELL), Barcelona, Spain; CIBER Fisiopatología Obesidad y Nutrición (CIBERobn), Instituto de Salud Carlos III, Barcelona, Spain
| | - Andrew Harkin
- School of Pharmacy and Pharmaceutical Sciences, Trinity College Dublin, Ireland
| | - Simone Macrì
- Centre for Behavioural Sciences and Mental Health, Istituto Superiore di Sanità, 00161 Rome, Italy
| | - Bernat Mora-Maltas
- Department of Psychiatry, University Hospital of Bellvitge, Barcelona, Spain; Psychoneurobiology of Eating and Addictive Behaviors Group, Neurosciences Program, Bellvitge Biomedical Research Institute (IDIBELL), Barcelona, Spain
| | - Susana Jiménez-Murcia
- Department of Psychiatry, University Hospital of Bellvitge, Barcelona, Spain; Psychoneurobiology of Eating and Addictive Behaviors Group, Neurosciences Program, Bellvitge Biomedical Research Institute (IDIBELL), Barcelona, Spain; CIBER Fisiopatología Obesidad y Nutrición (CIBERobn), Instituto de Salud Carlos III, Barcelona, Spain; Department of Clinical Sciences, School of Medicine and Health Sciences, University of Barcelona, Barcelona, Spain
| | - Aet O'Leary
- University Hospital Frankfurt, Frankfurt, Germany
| | - Angela Maria Ottomana
- Centre for Behavioural Sciences and Mental Health, Istituto Superiore di Sanità, 00161 Rome, Italy; Neuroscience Unit, Department of Medicine, University of Parma, 43100 Parma, Italy
| | - Martina Presta
- Centre for Behavioural Sciences and Mental Health, Istituto Superiore di Sanità, 00161 Rome, Italy; Department of Physiology and Pharmacology, Sapienza University of Rome, 00185 Rome, Italy
| | | | | | - Jeffrey C Glennon
- Conway Institute of Biomedical and Biomolecular Research, School of Medicine, University College Dublin, Dublin, Ireland
| |
Collapse
|
9
|
Kaur P, Kaur A. Review of Progress in Diagnostic Studies of Autism Spectrum Disorder Using Neuroimaging. Interdiscip Sci 2023; 15:111-130. [PMID: 36633792 DOI: 10.1007/s12539-022-00548-6] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/22/2022] [Revised: 12/27/2022] [Accepted: 12/27/2022] [Indexed: 01/13/2023]
Abstract
This review article summarizes the recent advances in the diagnostic studies of autism spectrum disorders (ASDs) considering some of the most influential research articles from the last two decades. ASD is a heterogeneous neurodevelopmental disorder characterized by abnormalities in social interaction, communication, and behavioral patterns as well as some unique strengths and differences. The current diagnosis systems are based on autism diagnostic observation schedule (ADOS) or autism diagnostic interview-revised (ADI-R), but biological markers are also important for an effective diagnosis of ASDs. The amalgamation of neuroimaging techniques, such as structural and functional magnetic resonance imaging (sMRI and fMRI), with machine-learning and deep-learning approaches helps throw new light on typical biological markers of ASDs at the early stage of life. To assess the performance of a deep neural network, we develop a light-weighted CNN model for ASD classification. The overall accuracy, precision, and F1-score of the proposed model are 99.92%, 99.93% and 99.92%, respectively. All the neuroimaging studies we have reviewed can be divided into 3 categories, viz. thickness, volume and functional connectivity-based studies. We conclude with a discussion of the major findings of considered studies and promising directions for future research in this field.
Collapse
Affiliation(s)
- Palwinder Kaur
- Department of Computer Science and Technology, Central University of Punjab, Bathinda, Punjab, 151001, India
| | - Amandeep Kaur
- Department of Computer Science and Technology, Central University of Punjab, Bathinda, Punjab, 151001, India.
| |
Collapse
|
10
|
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.
Collapse
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
| |
Collapse
|
11
|
Arutiunian V, Gomozova M, Minnigulova A, Davydova E, Pereverzeva D, Sorokin A, Tyushkevich S, Mamokhina U, Danilina K, Dragoy O. Structural brain abnormalities and their association with language impairment in school-aged children with Autism Spectrum Disorder. Sci Rep 2023; 13:1172. [PMID: 36670149 PMCID: PMC9860052 DOI: 10.1038/s41598-023-28463-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/27/2021] [Accepted: 01/18/2023] [Indexed: 01/22/2023] Open
Abstract
Language impairment is comorbid in most children with Autism Spectrum Disorder (ASD) but its neural basis is poorly understood. Using structural magnetic resonance imaging (MRI), the present study provides the whole-brain comparison of both volume- and surface-based characteristics between groups of children with and without ASD and investigates the relationships between these characteristics in language-related areas and the language abilities of children with ASD measured with standardized tools. A total of 36 school-aged children participated in the study: 18 children with ASD and 18 age- and sex-matched typically developing controls. The results revealed that multiple regions differed between groups of children in gray matter volume, gray matter thickness, gyrification, and cortical complexity (fractal dimension). White matter volume and sulcus depth did not differ between groups of children in any region. Importantly, gray matter thickness and gyrification of language-related areas were related to language functioning in children with ASD. Thus, the results of the present study shed some light on the structural brain abnormalities associated with language impairment in ASD.
Collapse
Affiliation(s)
- Vardan Arutiunian
- Center for Child Health, Behavior and Development, Seattle Children's Research Institute, 1920 Terry Ave., Seattle, WA, 98101, USA.
| | | | | | - Elizaveta Davydova
- Federal Resource Center for ASD, Moscow State University of Psychology and Education, Moscow, Russia.,Chair of Differential Psychology and Psychophysiology, Moscow State University of Psychology and Education, Moscow, Russia
| | - Darya Pereverzeva
- Federal Resource Center for ASD, Moscow State University of Psychology and Education, Moscow, Russia
| | - Alexander Sorokin
- Federal Resource Center for ASD, Moscow State University of Psychology and Education, Moscow, Russia.,Haskins Laboratories, New Haven, CT, USA
| | - Svetlana Tyushkevich
- Federal Resource Center for ASD, Moscow State University of Psychology and Education, Moscow, Russia
| | - Uliana Mamokhina
- Federal Resource Center for ASD, Moscow State University of Psychology and Education, Moscow, Russia
| | - Kamilla Danilina
- Federal Resource Center for ASD, Moscow State University of Psychology and Education, Moscow, Russia
| | - Olga Dragoy
- Center for Language and Brain, HSE University, Moscow, Russia.,Institute of Linguistics, Russian Academy of Sciences, Moscow, Russia
| |
Collapse
|
12
|
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.
Collapse
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.
| |
Collapse
|
13
|
Khadem-Reza ZK, Zare H. Automatic detection of autism spectrum disorder (ASD) in children using structural magnetic resonance imaging with machine vision system. MIDDLE EAST CURRENT PSYCHIATRY 2022. [DOI: 10.1186/s43045-022-00220-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/10/2022] Open
Abstract
Abstract
Background
Autism spectrum disorder (ASD) is a group of developmental disorders of the nervous system whose main manifestations are defects in social interactions, communication, repetitive behaviors, and limited interests. Over the years, the use of magnetic resonance imaging (MRI) to help identify patterns that are common in people with autism has increased for classification purposes. This study propose a method for classifying ASD patients versus controls using structural MRI information. In order to increase the accuracy of this method, the volume and surface features of the structural images are used simultaneously.
Results
The accuracy of diagnosis respectively was 86.29%, 71.15%, 86.53%, and 88.46% with SVM, RF, KNN, and ANN classifiers. The highest accuracy of diagnosis was obtained using ANN.
Conclusions
Since clinical evaluations for the diagnosis of autism are extremely time-consuming and depend on the expertise of a specialist, the importance of intelligent diagnosis of this disorder becomes clear. The aim of this study was to design an intelligent system to diagnose autism spectrum disorder.
Collapse
|
14
|
Bayer JMM, Dinga R, Kia SM, Kottaram AR, Wolfers T, Lv J, Zalesky A, Schmaal L, Marquand A. Accommodating site variation in neuroimaging data using normative and hierarchical Bayesian models. Neuroimage 2022; 264:119699. [PMID: 36272672 DOI: 10.1016/j.neuroimage.2022.119699] [Citation(s) in RCA: 15] [Impact Index Per Article: 7.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/10/2022] [Revised: 10/16/2022] [Accepted: 10/18/2022] [Indexed: 11/06/2022] Open
Abstract
The potential of normative modeling to make individualized predictions from neuroimaging data has enabled inferences that go beyond the case-control approach. However, site effects are often confounded with variables of interest in a complex manner and can bias estimates of normative models, which has impeded the application of normative models to large multi-site neuroimaging data sets. In this study, we suggest accommodating for these site effects by including them as random effects in a hierarchical Bayesian model. We compared the performance of a linear and a non-linear hierarchical Bayesian model in modeling the effect of age on cortical thickness. We used data of 570 healthy individuals from the ABIDE (autism brain imaging data exchange) data set in our experiments. In addition, we used data from individuals with autism to test whether our models are able to retain clinically useful information while removing site effects. We compared the proposed single stage hierarchical Bayesian method to several harmonization techniques commonly used to deal with additive and multiplicative site effects using a two stage regression, including regressing out site and harmonizing for site with ComBat, both with and without explicitly preserving variance caused by age and sex as biological variation of interest, and with a non-linear version of ComBat. In addition, we made predictions from raw data, in which site has not been accommodated for. The proposed hierarchical Bayesian method showed the best predictive performance according to multiple metrics. Beyond that, the resulting z-scores showed little to no residual site effects, yet still retained clinically useful information. In contrast, performance was particularly poor for the regression model and the ComBat model in which age and sex were not explicitly modeled. In all two stage harmonization models, predictions were poorly scaled, suffering from a loss of more than 90% of the original variance. Our results show the value of hierarchical Bayesian regression methods for accommodating site variation in neuroimaging data, which provides an alternative to harmonization techniques. While the approach we propose may have broad utility, our approach is particularly well suited to normative modeling where the primary interest is in accurate modeling of inter-subject variation and statistical quantification of deviations from a reference model.
Collapse
Affiliation(s)
- Johanna M M Bayer
- Orygen, Parkville, Australia; Centre for Youth mental Health, The University of Melbourne, Australia.
| | - Richard Dinga
- Donders Institute, Radboud University, Nijmegen, the Netherlands; Radboud University Medical Centre, Nijmegen, the Netherlands
| | - Seyed Mostafa Kia
- Donders Institute, Radboud University, Nijmegen, the Netherlands; Radboud University Medical Centre, Nijmegen, the Netherlands
| | - Akhil R Kottaram
- Orygen, Parkville, Australia; Centre for Youth mental Health, The University of Melbourne, Australia
| | | | - Jinglei Lv
- School of Biomedical Engineering & Brain and Mind Center, University of Sydney, Sydney, Australia
| | - Andrew Zalesky
- Melbourne Neuropsychiatry Centre, The University of Melbourne & Melbourne Health, Australia; Department of Biomedical Engineering, The University of Melbourne, Australia
| | - Lianne Schmaal
- Orygen, Parkville, Australia; Centre for Youth mental Health, The University of Melbourne, Australia
| | - Andre Marquand
- Radboud University Medical Centre, Nijmegen, the Netherlands; Donders Institute for Brain, Cognition and Behaviour, Radboud University Medical Centre, Nijmegen, the Netherlands; Institute of Psychiatry, Kings College London, London, UK
| |
Collapse
|
15
|
Bayer JMM, Dinga R, Kia SM, Kottaram AR, Wolfers T, Lv J, Zalesky A, Schmaal L, Marquand A. Accommodating site variation in neuroimaging data using normative and hierarchical Bayesian models. Neuroimage 2022; 264:119699. [PMID: 36272672 PMCID: PMC7614761 DOI: 10.1101/2021.02.09.430363] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/10/2022] [Revised: 10/16/2022] [Accepted: 10/18/2022] [Indexed: 09/12/2023] Open
Abstract
The potential of normative modeling to make individualized predictions from neuroimaging data has enabled inferences that go beyond the case-control approach. However, site effects are often confounded with variables of interest in a complex manner and can bias estimates of normative models, which has impeded the application of normative models to large multi-site neuroimaging data sets. In this study, we suggest accommodating for these site effects by including them as random effects in a hierarchical Bayesian model. We compared the performance of a linear and a non-linear hierarchical Bayesian model in modeling the effect of age on cortical thickness. We used data of 570 healthy individuals from the ABIDE (autism brain imaging data exchange) data set in our experiments. In addition, we used data from individuals with autism to test whether our models are able to retain clinically useful information while removing site effects. We compared the proposed single stage hierarchical Bayesian method to several harmonization techniques commonly used to deal with additive and multiplicative site effects using a two stage regression, including regressing out site and harmonizing for site with ComBat, both with and without explicitly preserving variance caused by age and sex as biological variation of interest, and with a non-linear version of ComBat. In addition, we made predictions from raw data, in which site has not been accommodated for. The proposed hierarchical Bayesian method showed the best predictive performance according to multiple metrics. Beyond that, the resulting z-scores showed little to no residual site effects, yet still retained clinically useful information. In contrast, performance was particularly poor for the regression model and the ComBat model in which age and sex were not explicitly modeled. In all two stage harmonization models, predictions were poorly scaled, suffering from a loss of more than 90% of the original variance. Our results show the value of hierarchical Bayesian regression methods for accommodating site variation in neuroimaging data, which provides an alternative to harmonization techniques. While the approach we propose may have broad utility, our approach is particularly well suited to normative modeling where the primary interest is in accurate modeling of inter-subject variation and statistical quantification of deviations from a reference model.
Collapse
Affiliation(s)
- Johanna M M Bayer
- Orygen, Parkville, Australia; Centre for Youth mental Health, The University of Melbourne, Australia.
| | - Richard Dinga
- Donders Institute, Radboud University, Nijmegen, the Netherlands; Radboud University Medical Centre, Nijmegen, the Netherlands
| | - Seyed Mostafa Kia
- Donders Institute, Radboud University, Nijmegen, the Netherlands; Radboud University Medical Centre, Nijmegen, the Netherlands
| | - Akhil R Kottaram
- Orygen, Parkville, Australia; Centre for Youth mental Health, The University of Melbourne, Australia
| | | | - Jinglei Lv
- School of Biomedical Engineering & Brain and Mind Center, University of Sydney, Sydney, Australia
| | - Andrew Zalesky
- Melbourne Neuropsychiatry Centre, The University of Melbourne & Melbourne Health, Australia; Department of Biomedical Engineering, The University of Melbourne, Australia
| | - Lianne Schmaal
- Orygen, Parkville, Australia; Centre for Youth mental Health, The University of Melbourne, Australia
| | - Andre Marquand
- Radboud University Medical Centre, Nijmegen, the Netherlands; Donders Institute for Brain, Cognition and Behaviour, Radboud University Medical Centre, Nijmegen, the Netherlands; Institute of Psychiatry, Kings College London, London, UK
| |
Collapse
|
16
|
Khadem-Reza ZK, Zare H. Evaluation of brain structure abnormalities in children with autism spectrum disorder (ASD) using structural magnetic resonance imaging. THE EGYPTIAN JOURNAL OF NEUROLOGY, PSYCHIATRY AND NEUROSURGERY 2022. [DOI: 10.1186/s41983-022-00576-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/19/2022] Open
Abstract
Abstract
Background
Autism spectrum disorder (ASD) is a group of developmental disorders of the nervous system. Since the core cause of many of the symptoms of autism spectrum disorder is due to changes in the structure of the brain, the importance of examining the structural abnormalities of the brain in these disorder becomes apparent. The aim of this study is evaluation of brain structure abnormalities in children with autism spectrum disorder (ASD) using structural magnetic resonance imaging (sMRI). sMRI images of 26 autistic and 26 Healthy control subjects in the range of 5–10 years are selected from the ABIDE database. For a better assessment of structural abnormalities, the surface and volume features are extracted together from this images. Then, the extracted features from both groups were compared with the sample t test and the features with significant differences between the two groups were identified.
Results
The results of volume-based features indicate an increase in total brain volume and white matter and a change in white and gray matter volume in brain regions of Hammers atlas in the autism group. In addition, the results of surface-based features indicate an increase in mean and standard deviation of cerebral cortex thickness and changes in cerebral cortex thickness, sulcus depth, surface complexity and gyrification index in the brain regions of the Desikan–Killany cortical atlas.
Conclusions
Identifying structurally abnormal areas of the brain and examining their relationship to the clinical features of Autism Spectrum Disorder can pave the way for the correct and early detection of this disorder using structural magnetic resonance imaging. It is also possible to design treatment for autistic people based on the abnormal areas of the brain, and to see the effectiveness of the treatment using imaging.
Collapse
|
17
|
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.
Collapse
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
| |
Collapse
|
18
|
Cortical and subcortical grey matter atrophy in Amyotrophic Lateral Sclerosis correlates with measures of disease accumulation independent of disease aggressiveness. Neuroimage Clin 2022; 36:103162. [PMID: 36067613 PMCID: PMC9460837 DOI: 10.1016/j.nicl.2022.103162] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/30/2022] [Revised: 07/11/2022] [Accepted: 08/18/2022] [Indexed: 12/14/2022]
Abstract
There is a growing demand for reliable biomarkers to monitor disease progression in Amyotrophic Lateral Sclerosis (ALS) that also take the heterogeneity of ALS into account. In this study, we explored the association between Magnetic Resonance Imaging (MRI)-derived measures of cortical thickness (CT) and subcortical grey matter (GM) volume with D50 model parameters. T1-weighted MRI images of 72 Healthy Controls (HC) and 100 patients with ALS were analyzed using Surface-based Morphometry for cortical structures and Voxel-based Morphometry for subcortical Region-Of-Interest analyses using the Computational Anatomy Toolbox (CAT12). In Inter-group contrasts, these parameters were compared between patients and HC. Further, the D50 model was used to conduct subgroup-analyses, dividing patients by a) Phase of disease covered at the time of MRI-scan and b) individual overall disease aggressiveness. Finally, correlations between GM and D50 model-derived parameters were examined. Inter-group analyses revealed ALS-related cortical thinning compared to HC located mainly in frontotemporal regions and a decrease in GM volume in the left hippocampus and amygdala. A comparison of patients in different phases showed further cortical and subcortical GM atrophy along with disease progression. Correspondingly, regression analyses identified negative correlations between cortical thickness and individual disease covered. However, there were no differences in CT and subcortical GM between patients with low and high disease aggressiveness. By application of the D50 model, we identified correlations between cortical and subcortical GM atrophy and ALS-related functional disability, but not with disease aggressiveness. This qualifies CT and subcortical GM volume as biomarkers representing individual disease covered to monitor therapeutic interventions in ALS.
Collapse
|
19
|
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 Clin 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] [Key Words] [MESH Headings] [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]
Abstract
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.
Collapse
Affiliation(s)
- Sara Saponaro
- University of Pisa, Pisa, Italy; National Institute for Nuclear Physics (INFN), Pisa Division, Pisa, Italy
| | - Alessia Giuliano
- Medical Physics Department, San Luca Hospital, 55100 Lucca, Italy
| | - Roberto Bellotti
- Physics Department, University of Bari Aldo Moro, Bari, Italy; National Institute of Nuclear Physics (INFN), Bari Division, Bari, Italy
| | - Angela Lombardi
- Physics Department, University of Bari Aldo Moro, Bari, Italy; National Institute of Nuclear Physics (INFN), Bari Division, Bari, Italy.
| | - Sabina Tangaro
- National Institute of Nuclear Physics (INFN), Bari Division, Bari, Italy; Department of Soil, Plant and Food Sciences (DISSPA), University of Bari Aldo Moro, Bari, Italy
| | - Piernicola Oliva
- Department of Chemistry and Pharmacy, University of Sassari, Sassari, Italy; National Institute for Nuclear Physics (INFN), Cagliari Division, Cagliari, Italy
| | - Sara Calderoni
- Developmental Psychiatry Unit - IRCCS Stella Maris Foundation, Pisa, Italy; Department of Clinical and Experimental Medicine, University of Pisa, Pisa, Italy
| | - Alessandra Retico
- National Institute for Nuclear Physics (INFN), Pisa Division, Pisa, Italy
| |
Collapse
|
20
|
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: 14] [Impact Index Per Article: 7.0] [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.
Collapse
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
| |
Collapse
|
21
|
Shiohama T, Ortug A, Warren JLA, Valli B, Levman J, Faja SK, Tsujimura K, Maunakea AK, Takahashi E. Small Nucleus Accumbens and Large Cerebral Ventricles in Infants and Toddlers Prior to Receiving Diagnoses of Autism Spectrum Disorder. Cereb Cortex 2022; 32:1200-1211. [PMID: 34455432 PMCID: PMC8924432 DOI: 10.1093/cercor/bhab283] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/26/2021] [Revised: 07/17/2021] [Accepted: 07/18/2021] [Indexed: 11/14/2022] Open
Abstract
Early interventions for autism spectrum disorder (ASD) are increasingly available, while only 42-50% of ASD children are diagnosed before 3 years old (YO). To identify neuroimaging biomarkers for early ASD diagnosis, we evaluated surface- and voxel-based brain morphometry in participants under 3YO who were later diagnosed with ASD. Magnetic resonance imaging data were retrospectively obtained from patients later diagnosed with ASD at Boston Children's Hospital. The ASD participants with comorbidities such as congenital disorder, epilepsy, and global developmental delay/intellectual disability were excluded from statistical analyses. Eighty-five structural brain magnetic resonance imaging images were collected from 81 participants under 3YO and compared with 45 images from 45 gender- and age-matched nonautistic controls (non-ASD). Using an Infant FreeSurfer pipeline, 236 regionally distributed measurements were extracted from each scan. By t-tests and linear mixed models, the smaller nucleus accumbens and larger bilateral lateral, third, and fourth ventricles were identified in the ASD group. Vertex-wise t-statistical maps showed decreased thickness in the caudal anterior cingulate cortex and increased thickness in the right medial orbitofrontal cortex in ASD. The smaller bilateral accumbens nuclei and larger cerebral ventricles were independent of age, gender, or gestational age at birth, suggesting that there are MRI-based biomarkers in prospective ASD patients before they receive the diagnosis and that the volume of the nucleus accumbens and cerebral ventricles can be key MRI-based early biomarkers to predict the emergence of ASD.
Collapse
Affiliation(s)
- Tadashi Shiohama
- Division of Newborn Medicine, Department of Medicine, Boston Children's Hospital, Harvard Medical School, Boston, MA 02115, USA.,Department of Pediatrics, Chiba University Hospital, Chiba 2608670, Japan
| | - Alpen Ortug
- Division of Newborn Medicine, Department of Medicine, Boston Children's Hospital, Harvard Medical School, Boston, MA 02115, USA.,Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Charlestown, Massachusetts, USA.,Department of Radiology, Harvard Medical School, Boston, Massachusetts, USA
| | - Jose Luis Alatorre Warren
- Division of Newborn Medicine, Department of Medicine, Boston Children's Hospital, Harvard Medical School, Boston, MA 02115, USA.,Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Charlestown, Massachusetts, USA.,Department of Radiology, Harvard Medical School, Boston, Massachusetts, USA
| | - Briana Valli
- Division of Newborn Medicine, Department of Medicine, Boston Children's Hospital, Harvard Medical School, Boston, MA 02115, USA.,Behavioral Neuroscience Program, Northeastern University, Boston, MA 02115, USA
| | - Jacob Levman
- Department of Mathematics, Statistics and Computer Science, St. Francis Xavier University, Antigonish, Nova Scotia B2G 2W5, Canada
| | - Susan K Faja
- Division of Developmental Medicine, Department of Medicine, Boston Children's Hospital, Harvard Medical School, Boston, MA 02115, USA
| | - Keita Tsujimura
- Group of Brain Function and Development, Nagoya University Neuroscience Institute of the Graduate School of Science, Furo-cho, Chikusa-ku, Nagoya, Aichi 464-8602, Japan.,Research Unit for Developmental Disorders, Institute for Advanced Research, Nagoya University, Furo-cho, Chikusa-ku, Nagoya, Aichi 464-8602, Japan
| | - Alika K Maunakea
- Department of Anatomy, Biochemistry and Physiology, 651 Ilalo Street, John A. Burns School of Medicine, University of Hawaii, Manoa, HI 96813, USA
| | - Emi Takahashi
- Division of Newborn Medicine, Department of Medicine, Boston Children's Hospital, Harvard Medical School, Boston, MA 02115, USA.,Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Charlestown, Massachusetts, USA.,Department of Radiology, Harvard Medical School, Boston, Massachusetts, USA
| |
Collapse
|
22
|
Lucibello S, Bertè G, Verdolotti T, Lucignani M, Napolitano A, D’Abronzo R, Cicala MG, Pede E, Chieffo D, Mariotti P, Colosimo C, Mercuri E, Battini R. Cortical Thickness and Clinical Findings in Prescholar Children With Autism Spectrum Disorder. Front Neurosci 2022; 15:776860. [PMID: 35197818 PMCID: PMC8858962 DOI: 10.3389/fnins.2021.776860] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/14/2021] [Accepted: 12/16/2021] [Indexed: 11/13/2022] Open
Abstract
The term autism spectrum disorder (ASD) includes a wide variability of clinical presentation, and this clinical heterogeneity seems to reflect a still unclear multifactorial etiopathogenesis, encompassing different genetic risk factors and susceptibility to environmental factors. Several studies and many theories recognize as mechanisms of autism a disruption of brain development and maturation time course, suggesting the existence of common neurobiological substrates, such as defective synaptic structure and aberrant brain connectivity. Magnetic resonance imaging (MRI) plays an important role in both assessment of region-specific structural changes and quantification of specific alterations in gray or white matter, which could lead to the identification of an MRI biomarker. In this study, we performed measurement of cortical thickness in a selected well-known group of preschool ASD subjects with the aim of finding correlation between cortical metrics and clinical scores to understand the underlying mechanism of symptoms and to support early clinical diagnosis. Our results confirm that recent brain MRI techniques combined with clinical data can provide some useful information in defining the cerebral regions involved in ASD although large sample studies with homogeneous analytical and multisite approaches are needed.
Collapse
Affiliation(s)
- Simona Lucibello
- Pediatric Neurology Unit, Fondazione Policlinico Universitario A. Gemelli IRCCS, Rome, Italy
| | - Giovanna Bertè
- Dipartimento di Diagnostica per Immagini, Istituto di Radiologia, Università Cattolica del Sacro Cuore, Rome, Italy
| | - Tommaso Verdolotti
- UOC Radiologia e Neuroradiologia, Fondazione Policlinico Universitario A. Gemelli IRCCS, Rome, Italy
| | - Martina Lucignani
- Medical Physics Unit, Bambino Gesù Children’s Hospital, IRCCS, Rome, Italy
| | - Antonio Napolitano
- Medical Physics Unit, Bambino Gesù Children’s Hospital, IRCCS, Rome, Italy
| | - Rosa D’Abronzo
- Dipartimento di Diagnostica per Immagini, Istituto di Radiologia, Università Cattolica del Sacro Cuore, Rome, Italy
| | - Maria G. Cicala
- Pediatric Neurology Unit, Fondazione Policlinico Universitario A. Gemelli IRCCS, Rome, Italy
| | - Elisa Pede
- Pediatric Neurology Unit, Fondazione Policlinico Universitario A. Gemelli IRCCS, Rome, Italy
| | - Daniela Chieffo
- Pediatric Neurology Unit, Fondazione Policlinico Universitario A. Gemelli IRCCS, Rome, Italy
| | - Paolo Mariotti
- Pediatric Neurology Unit, Fondazione Policlinico Universitario A. Gemelli IRCCS, Rome, Italy
| | - Cesare Colosimo
- Dipartimento di Diagnostica per Immagini, Istituto di Radiologia, Università Cattolica del Sacro Cuore, Rome, Italy
- UOC Radiologia e Neuroradiologia, Fondazione Policlinico Universitario A. Gemelli IRCCS, Rome, Italy
| | - Eugenio Mercuri
- Pediatric Neurology Unit, Fondazione Policlinico Universitario A. Gemelli IRCCS, Rome, Italy
- Centro Clinico Nemo, Fondazione Policlinico Universitario A. Gemelli IRCCS, Rome, Italy
| | - Roberta Battini
- Department of Clinical and Experimental Medicine, University of Pisa, Pisa, Italy
- Department of Developmental Neuroscience, IRCCS Fondazione Stella Maris, Pisa, Italy
- *Correspondence: Roberta Battini,
| |
Collapse
|
23
|
Ali MT, ElNakieb Y, Elnakib A, Shalaby A, Mahmoud A, Ghazal M, Yousaf J, Abu Khalifeh H, Casanova M, Barnes G, El-Baz A. The Role of Structure MRI in Diagnosing Autism. Diagnostics (Basel) 2022; 12:165. [PMID: 35054330 PMCID: PMC8774643 DOI: 10.3390/diagnostics12010165] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/09/2021] [Revised: 12/30/2021] [Accepted: 01/05/2022] [Indexed: 12/30/2022] Open
Abstract
This study proposes a Computer-Aided Diagnostic (CAD) system to diagnose subjects with autism spectrum disorder (ASD). The CAD system identifies morphological anomalies within the brain regions of ASD subjects. Cortical features are scored according to their contribution in diagnosing a subject to be ASD or typically developed (TD) based on a trained machine-learning (ML) model. This approach opens the hope for developing a new CAD system for early personalized diagnosis of ASD. We propose a framework to extract the cerebral cortex from structural MRI as well as identifying the altered areas in the cerebral cortex. This framework consists of the following five main steps: (i) extraction of cerebral cortex from structural MRI; (ii) cortical parcellation to a standard atlas; (iii) identifying ASD associated cortical markers; (iv) adjusting feature values according to sex and age; (v) building tailored neuro-atlases to identify ASD; and (vi) artificial neural networks (NN) are trained to classify ASD. The system is tested on the Autism Brain Imaging Data Exchange (ABIDE I) sites achieving an average balanced accuracy score of 97±2%. This paper demonstrates the ability to develop an objective CAD system using structure MRI and tailored neuro-atlases describing specific developmental patterns of the brain in autism.
Collapse
Affiliation(s)
- Mohamed T. Ali
- Bioengineering Department, University of Louisville, Louisville, KY 40208, USA; (M.T.A.); (Y.E.); (A.E.); (A.S.); (A.M.)
| | - Yaser ElNakieb
- Bioengineering Department, University of Louisville, Louisville, KY 40208, USA; (M.T.A.); (Y.E.); (A.E.); (A.S.); (A.M.)
| | - Ahmed Elnakib
- Bioengineering Department, University of Louisville, Louisville, KY 40208, USA; (M.T.A.); (Y.E.); (A.E.); (A.S.); (A.M.)
| | - Ahmed Shalaby
- Bioengineering Department, University of Louisville, Louisville, KY 40208, USA; (M.T.A.); (Y.E.); (A.E.); (A.S.); (A.M.)
| | - Ali Mahmoud
- Bioengineering Department, University of Louisville, Louisville, KY 40208, USA; (M.T.A.); (Y.E.); (A.E.); (A.S.); (A.M.)
| | - Mohammed Ghazal
- Department of Electrical and Computer Engineering, Abu Dhabi University, Abu Dhabi 59911, United Arab Emirates; (M.G.); (J.Y.); (H.A.K.)
| | - Jawad Yousaf
- Department of Electrical and Computer Engineering, Abu Dhabi University, Abu Dhabi 59911, United Arab Emirates; (M.G.); (J.Y.); (H.A.K.)
| | - Hadil Abu Khalifeh
- Department of Electrical and Computer Engineering, Abu Dhabi University, Abu Dhabi 59911, United Arab Emirates; (M.G.); (J.Y.); (H.A.K.)
| | - Manuel Casanova
- Department of Biomedical Sciences, School of Medicine Greenville, University of South Carolina, Greenville, SC 29425, USA;
| | - Gregory Barnes
- Department of Neurology, Norton Children’s Autism Center, University of Louisville, Louisville, KY 40208, USA;
| | - Ayman El-Baz
- Bioengineering Department, University of Louisville, Louisville, KY 40208, USA; (M.T.A.); (Y.E.); (A.E.); (A.S.); (A.M.)
| |
Collapse
|
24
|
Arunachalam Chandran V, Pliatsikas C, Neufeld J, O'Connell G, Haffey A, DeLuca V, Chakrabarti B. Brain structural correlates of autistic traits across the diagnostic divide: A grey matter and white matter microstructure study. Neuroimage Clin 2021; 32:102897. [PMID: 34911200 PMCID: PMC8641248 DOI: 10.1016/j.nicl.2021.102897] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/28/2021] [Revised: 10/25/2021] [Accepted: 11/22/2021] [Indexed: 12/02/2022]
Abstract
Autism Spectrum Disorders (ASD) are a set of neurodevelopmental conditions characterised by difficulties in social interaction and communication as well as stereotyped and restricted patterns of interest. Autistic traits exist in a continuum across the general population, whilst the extreme end of this distribution is diagnosed as clinical ASD. While many studies have investigated brain structure in autism using a case-control design, few have used a dimensional approach. To add to this growing body of literature, we investigated the structural brain correlates of autistic traits in a mixed sample of adult participants (25 ASD and 66 neurotypicals; age: 18-60 years). We examined the relationship between regional brain volumes (using voxel-based morphometry and surface-based morphometry) and white matter microstructure properties (using Diffusion Tensor Imaging) and autistic traits (using Autism Spectrum Quotient). Our findings show grey matter differences in regions including the orbitofrontal cortex and lingual gyrus, and suggestive evidence for white matter microstructure differences in tracts including the superior longitudinal fasciculus being related to higher autistic traits. These grey matter and white matter microstructure findings from our study are consistent with previous reports and support the brain structural differences in ASD. These findings provide further support for shared aetiology for autistic traits across the diagnostic divide.
Collapse
Affiliation(s)
- Varun Arunachalam Chandran
- Centre for Autism, School of Psychology and Clinical Language Sciences (SPCLS), University of Reading, UK; Center for Mind and Brain, University of California Davis, Davis, CA, USA.
| | - Christos Pliatsikas
- School of Psychology and Clinical Language Sciences, University of Reading, Harry Pitt Building, Earley Gate, Whiteknights Road, Reading RG6 6AL, UK; Centro de Ciencia Cognitiva, Facultad de Lenguas y Educación, Universidad Antonio de Nebrija, Calle de Sta. Cruz de Marcenado, 27, 28015 Madrid, Spain
| | - Janina Neufeld
- Center of Neurodevelopmental Disorders (KIND), Centre for Psychiatry Research, Department of Women's and Children's Health, Karolinska Institutet & Stockholm Health Care Services, Stockholm, Sweden
| | | | - Anthony Haffey
- Centre for Autism, School of Psychology and Clinical Language Sciences (SPCLS), University of Reading, UK
| | - Vincent DeLuca
- Department of Language and Culture, UiT- The Arctic University of Norway, Hansine Hansens veg 18, 9019 Tromsø, Norway
| | - Bhismadev Chakrabarti
- Centre for Autism, School of Psychology and Clinical Language Sciences (SPCLS), University of Reading, UK; Department of Psychology, Ashoka University, Sonipat, India; India Autism Center, Kolkata, India
| |
Collapse
|
25
|
Wang Z, Peng D, Shang Y, Gao J. Autistic Spectrum Disorder Detection and Structural Biomarker Identification Using Self-Attention Model and Individual-Level Morphological Covariance Brain Networks. Front Neurosci 2021; 15:756868. [PMID: 34712116 PMCID: PMC8547518 DOI: 10.3389/fnins.2021.756868] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/11/2021] [Accepted: 09/06/2021] [Indexed: 11/13/2022] Open
Abstract
Autism spectrum disorder (ASD) is a range of neurodevelopmental disorders, which brings enormous burdens to the families of patients and society. However, due to the lack of representation of variance for diseases and the absence of biomarkers for diagnosis, the early detection and intervention of ASD are remarkably challenging. In this study, we proposed a self-attention deep learning framework based on the transformer model on structural MR images from the ABIDE consortium to classify ASD patients from normal controls and simultaneously identify the structural biomarkers. In our work, the individual structural covariance networks are used to perform ASD/NC classification via a self-attention deep learning framework, instead of the original structural MR data, to take full advantage of the coordination patterns of morphological features between brain regions. The self-attention deep learning framework based on the transformer model can extract both local and global information from the input data, making it more suitable for the brain network data than the CNN- structural model. Meanwhile, the potential diagnosis structural biomarkers are identified by the self-attention coefficients map. The experimental results showed that our proposed method outperforms most of the current methods for classifying ASD patients with the ABIDE data and achieves a classification accuracy of 72.5% across different sites. Furthermore, the potential diagnosis biomarkers were found mainly located in the prefrontal cortex, temporal cortex, and cerebellum, which may be treated as the early biomarkers for the ASD diagnosis. Our study demonstrated that the self-attention deep learning framework is an effective way to diagnose ASD and establish the potential biomarkers for ASD.
Collapse
Affiliation(s)
- Zhengning Wang
- School of Information and Communication Engineering, University of Electronic Science and Technology of China, Chengdu, China
| | - Dawei Peng
- School of Information and Communication Engineering, University of Electronic Science and Technology of China, Chengdu, China
| | - Yongbin Shang
- School of Information and Communication Engineering, University of Electronic Science and Technology of China, Chengdu, China
| | - Jingjing Gao
- School of Information and Communication Engineering, University of Electronic Science and Technology of China, Chengdu, China
| |
Collapse
|
26
|
Machine learning with neuroimaging data to identify autism spectrum disorder: a systematic review and meta-analysis. Neuroradiology 2021; 63:2057-2072. [PMID: 34420058 DOI: 10.1007/s00234-021-02774-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/19/2020] [Accepted: 07/14/2021] [Indexed: 10/20/2022]
Abstract
PURPOSE Autism Spectrum Disorder (ASD) is diagnosed through observation or interview assessments, which is time-consuming, subjective, and with questionable validity and reliability. Thus, we aimed to evaluate the role of machine learning (ML) with neuroimaging data to provide a reliable classification of ASD. METHODS A systematic search of PubMed, Scopus, and Embase was conducted to identify relevant publications. Quality Assessment of Diagnostic Accuracy Studies-2 (QUADAS-2) was used to assess the studies' quality. A bivariate random-effects model meta-analysis was employed to evaluate the pooled sensitivity, the pooled specificity, and the diagnostic performance through the hierarchical summary receiver operating characteristic (HSROC) curve of ML with neuroimaging data in classifying ASD. Meta-regression was also performed. RESULTS Forty-four studies (5697 ASD and 6013 typically developing individuals [TD] in total) were included in the quantitative analysis. The pooled sensitivity for differentiating ASD from TD individuals was 86.25 95% confidence interval [CI] (81.24, 90.08), while the pooled specificity was 83.31 95% CI (78.12, 87.48) with a combined area under the HSROC (AUC) of 0.889. Higgins I2 (> 90%) and Cochran's Q (p < 0.0001) suggest a high degree of heterogeneity. In the bivariate model meta-regression, a higher pooled specificity was observed in studies not using a brain atlas (90.91 95% CI [80.67, 96.00], p = 0.032). In addition, a greater pooled sensitivity was seen in studies recruiting both males and females (89.04 95% CI [83.84, 92.72], p = 0.021), and combining imaging modalities (94.12 95% [85.43, 97.76], p = 0.036). CONCLUSION ML with neuroimaging data is an exciting prospect in detecting individuals with ASD but further studies are required to improve its reliability for usage in clinical practice.
Collapse
|
27
|
Scherrer B, Prohl AK, Taquet M, Kapur K, Peters JM, Tomas-Fernandez X, Davis PE, M Bebin E, Krueger DA, Northrup H, Y Wu J, Sahin M, Warfield SK. The Connectivity Fingerprint of the Fusiform Gyrus Captures the Risk of Developing Autism in Infants with Tuberous Sclerosis Complex. Cereb Cortex 2021; 30:2199-2214. [PMID: 31812987 DOI: 10.1093/cercor/bhz233] [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: 07/27/2018] [Revised: 09/05/2019] [Accepted: 09/12/2019] [Indexed: 12/13/2022] Open
Abstract
Tuberous sclerosis complex (TSC) is a rare genetic disorder characterized by benign tumors throughout the body; it is generally diagnosed early in life and has a high prevalence of autism spectrum disorder (ASD), making it uniquely valuable in studying the early development of autism, before neuropsychiatric symptoms become apparent. One well-documented deficit in ASD is an impairment in face processing. In this work, we assessed whether anatomical connectivity patterns of the fusiform gyrus, a central structure in face processing, capture the risk of developing autism early in life. We longitudinally imaged TSC patients at 1, 2, and 3 years of age with diffusion compartment imaging. We evaluated whether the anatomical connectivity fingerprint of the fusiform gyrus was associated with the risk of developing autism measured by the Autism Observation Scale for Infants (AOSI). Our findings suggest that the fusiform gyrus connectivity captures the risk of developing autism as early as 1 year of age and provides evidence that abnormal fusiform gyrus connectivity increases with age. Moreover, the identified connections that best capture the risk of developing autism involved the fusiform gyrus and limbic and paralimbic regions that were consistent with the ASD phenotype, involving an increased number of left-lateralized structures with increasing age.
Collapse
Affiliation(s)
- Benoit Scherrer
- Computational Radiology Laboratory, Department of Radiology, Boston Children's Hospital, Harvard Medical School, Boston, MA, 02115 USA
| | - Anna K Prohl
- Computational Radiology Laboratory, Department of Radiology, Boston Children's Hospital, Harvard Medical School, Boston, MA, 02115 USA
| | - Maxime Taquet
- Computational Radiology Laboratory, Department of Radiology, Boston Children's Hospital, Harvard Medical School, Boston, MA, 02115 USA
| | - Kush Kapur
- Department of Neurology, Boston Children's Hospital, Harvard Medical School, Boston, MA, 02115 USA
| | - Jurriaan M Peters
- Department of Neurology, Boston Children's Hospital, Harvard Medical School, Boston, MA, 02115 USA
| | - Xavier Tomas-Fernandez
- Computational Radiology Laboratory, Department of Radiology, Boston Children's Hospital, Harvard Medical School, Boston, MA, 02115 USA
| | - Peter E Davis
- Department of Neurology, Boston Children's Hospital, Harvard Medical School, Boston, MA, 02115 USA
| | - Elizabeth M Bebin
- Department of Neurology, University of Alabama at Birmingham, Birmingham, AL, 35233 USA
| | - Darcy A Krueger
- Department of Neurology, Cincinnati Children's Hospital Medical Center, Cincinnati, OH, 45229 USA
| | - Hope Northrup
- Department of Pediatrics, McGovern Medical School, University of Texas Health Science Center at Houston, Houston, TX, 77030 USA
| | - Joyce Y Wu
- Department of Pediatrics, UCLA Mattel Children's Hospital, David Geffen School of Medicine, University of California Los Angeles, Los Angeles, CA, 90095 USA
| | - Mustafa Sahin
- Department of Neurology, Boston Children's Hospital, Harvard Medical School, Boston, MA, 02115 USA
| | - Simon K Warfield
- Computational Radiology Laboratory, Department of Radiology, Boston Children's Hospital, Harvard Medical School, Boston, MA, 02115 USA
| |
Collapse
|
28
|
Liu Y, Xu L, Yu J, Li J, Yu X. Identification of autism spectrum disorder using multi-regional resting-state data through an attention learning approach. Biomed Signal Process Control 2021. [DOI: 10.1016/j.bspc.2021.102833] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/21/2022]
|
29
|
Squarcina L, Nosari G, Marin R, Castellani U, Bellani M, Bonivento C, Fabbro F, Molteni M, Brambilla P. Automatic classification of autism spectrum disorder in children using cortical thickness and support vector machine. Brain Behav 2021; 11:e2238. [PMID: 34264004 PMCID: PMC8413814 DOI: 10.1002/brb3.2238] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/04/2020] [Revised: 05/10/2021] [Accepted: 05/23/2021] [Indexed: 12/11/2022] Open
Abstract
OBJECTIVE Autism spectrum disorder (ASD) is a neurodevelopmental condition with a heterogeneous phenotype. The role of biomarkers in ASD diagnosis has been highlighted; cortical thickness has proved to be involved in the etiopathogenesis of ASD core symptoms. We apply support vector machine, a supervised machine learning method, in order to identify specific cortical thickness alterations in ASD subjects. METHODS A sample of 76 subjects (9.5 ± 3.4 years old) has been selected, 40 diagnosed with ASD and 36 typically developed subjects. All children underwent a magnetic resonance imaging (MRI) examination; T1-MPRAGE sequences were analyzed to extract features for the characterization and parcellation of regions of interests (ROI); average cortical thickness (CT) has been measured for each ROI. For the classification process, the extracted features were used as input for a classifier to identify ASD subjects through a "learning by example" procedure; the features with best performance was then selected by "greedy forward-feature selection." Finally, this model underwent a leave-one-out cross-validation approach. RESULTS From the training set of 68 ROIs, five ROIs reached accuracies of over 70%. After this phase, we used a recursive feature selection process in order to identify the eight features with the best accuracy (84.2%). CT resulted higher in ASD compared to controls in all the ROIs identified at the end of the process. CONCLUSION We found increased CT in various brain regions in ASD subjects, confirming their role in the pathogenesis of this condition. Considering the brain development curve during ages, these changes in CT may normalize during development. Further validation on a larger sample is required.
Collapse
Affiliation(s)
- Letizia Squarcina
- Department of Pathophysiology and TransplantationUniversity of MilanVia Festa del Perdono, 7, 20122 MilanItaly
| | - Guido Nosari
- Department of Pathophysiology and TransplantationUniversity of MilanVia Festa del Perdono, 7, 20122 MilanItaly
| | - Riccardo Marin
- Department of InformaticsUniversity of VeronaVeronaItaly
| | | | - Marcella Bellani
- Department of NeurosciencesBiomedicine and Movement SciencesSection of PsychiatryUniversity of VeronaVeronaItaly
| | - Carolina Bonivento
- IRCCS “E. Medea”, Polo Friuli Venezia GiuliaSan Vito al Tagliamento (PN)Italy
| | | | - Massimo Molteni
- IRCCS “E. Medea”, Polo Friuli Venezia GiuliaSan Vito al Tagliamento (PN)Italy
| | - Paolo Brambilla
- Department of Pathophysiology and TransplantationUniversity of MilanVia Festa del Perdono, 7, 20122 MilanItaly
- Department of Neurosciences and Mental Health Department of Neurosciences and Mental HealthFondazione IRCCS Ca' Granda Ospedale Maggiore Policlinicovia Francesco Sforza 28, 20122 MilanItaly
| |
Collapse
|
30
|
Grzenda A, Kraguljac NV, McDonald WM, Nemeroff C, Torous J, Alpert JE, Rodriguez CI, Widge AS. Evaluating the Machine Learning Literature: A Primer and User's Guide for Psychiatrists. Am J Psychiatry 2021; 178:715-729. [PMID: 34080891 DOI: 10.1176/appi.ajp.2020.20030250] [Citation(s) in RCA: 20] [Impact Index Per Article: 6.7] [Reference Citation Analysis] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 11/30/2022]
Affiliation(s)
- Adrienne Grzenda
- Department of Psychiatry and Biobehavioral Sciences, David Geffen School of Medicine, University of California, Los Angeles, and Olive View-UCLA Medical Center, Sylmar (Grzenda); Department of Psychiatry and Behavioral Neurobiology, University of Alabama at Birmingham (Kraguljac); Department of Psychiatry and Behavioral Sciences, Emory University School of Medicine, Atlanta (McDonald); Department of Psychiatry, University of Texas Dell Medical School, Austin (Nemeroff); Department of Psychiatry, Beth Israel Deaconess Medical Center, Harvard Medical School, Boston (Torous); Department of Psychiatry and Behavioral Sciences, Albert Einstein School of Medicine, Bronx, N.Y. (Alpert); Department of Psychiatry and Behavioral Sciences, Stanford University, Stanford, Calif., and Veterans Affairs Palo Alto Health Care System, Palo Alto, Calif. (Rodriguez); Department of Psychiatry and Behavioral Sciences, University of Minnesota, Minneapolis (Widge)
| | - Nina V Kraguljac
- Department of Psychiatry and Biobehavioral Sciences, David Geffen School of Medicine, University of California, Los Angeles, and Olive View-UCLA Medical Center, Sylmar (Grzenda); Department of Psychiatry and Behavioral Neurobiology, University of Alabama at Birmingham (Kraguljac); Department of Psychiatry and Behavioral Sciences, Emory University School of Medicine, Atlanta (McDonald); Department of Psychiatry, University of Texas Dell Medical School, Austin (Nemeroff); Department of Psychiatry, Beth Israel Deaconess Medical Center, Harvard Medical School, Boston (Torous); Department of Psychiatry and Behavioral Sciences, Albert Einstein School of Medicine, Bronx, N.Y. (Alpert); Department of Psychiatry and Behavioral Sciences, Stanford University, Stanford, Calif., and Veterans Affairs Palo Alto Health Care System, Palo Alto, Calif. (Rodriguez); Department of Psychiatry and Behavioral Sciences, University of Minnesota, Minneapolis (Widge)
| | - William M McDonald
- Department of Psychiatry and Biobehavioral Sciences, David Geffen School of Medicine, University of California, Los Angeles, and Olive View-UCLA Medical Center, Sylmar (Grzenda); Department of Psychiatry and Behavioral Neurobiology, University of Alabama at Birmingham (Kraguljac); Department of Psychiatry and Behavioral Sciences, Emory University School of Medicine, Atlanta (McDonald); Department of Psychiatry, University of Texas Dell Medical School, Austin (Nemeroff); Department of Psychiatry, Beth Israel Deaconess Medical Center, Harvard Medical School, Boston (Torous); Department of Psychiatry and Behavioral Sciences, Albert Einstein School of Medicine, Bronx, N.Y. (Alpert); Department of Psychiatry and Behavioral Sciences, Stanford University, Stanford, Calif., and Veterans Affairs Palo Alto Health Care System, Palo Alto, Calif. (Rodriguez); Department of Psychiatry and Behavioral Sciences, University of Minnesota, Minneapolis (Widge)
| | - Charles Nemeroff
- Department of Psychiatry and Biobehavioral Sciences, David Geffen School of Medicine, University of California, Los Angeles, and Olive View-UCLA Medical Center, Sylmar (Grzenda); Department of Psychiatry and Behavioral Neurobiology, University of Alabama at Birmingham (Kraguljac); Department of Psychiatry and Behavioral Sciences, Emory University School of Medicine, Atlanta (McDonald); Department of Psychiatry, University of Texas Dell Medical School, Austin (Nemeroff); Department of Psychiatry, Beth Israel Deaconess Medical Center, Harvard Medical School, Boston (Torous); Department of Psychiatry and Behavioral Sciences, Albert Einstein School of Medicine, Bronx, N.Y. (Alpert); Department of Psychiatry and Behavioral Sciences, Stanford University, Stanford, Calif., and Veterans Affairs Palo Alto Health Care System, Palo Alto, Calif. (Rodriguez); Department of Psychiatry and Behavioral Sciences, University of Minnesota, Minneapolis (Widge)
| | - John Torous
- Department of Psychiatry and Biobehavioral Sciences, David Geffen School of Medicine, University of California, Los Angeles, and Olive View-UCLA Medical Center, Sylmar (Grzenda); Department of Psychiatry and Behavioral Neurobiology, University of Alabama at Birmingham (Kraguljac); Department of Psychiatry and Behavioral Sciences, Emory University School of Medicine, Atlanta (McDonald); Department of Psychiatry, University of Texas Dell Medical School, Austin (Nemeroff); Department of Psychiatry, Beth Israel Deaconess Medical Center, Harvard Medical School, Boston (Torous); Department of Psychiatry and Behavioral Sciences, Albert Einstein School of Medicine, Bronx, N.Y. (Alpert); Department of Psychiatry and Behavioral Sciences, Stanford University, Stanford, Calif., and Veterans Affairs Palo Alto Health Care System, Palo Alto, Calif. (Rodriguez); Department of Psychiatry and Behavioral Sciences, University of Minnesota, Minneapolis (Widge)
| | - Jonathan E Alpert
- Department of Psychiatry and Biobehavioral Sciences, David Geffen School of Medicine, University of California, Los Angeles, and Olive View-UCLA Medical Center, Sylmar (Grzenda); Department of Psychiatry and Behavioral Neurobiology, University of Alabama at Birmingham (Kraguljac); Department of Psychiatry and Behavioral Sciences, Emory University School of Medicine, Atlanta (McDonald); Department of Psychiatry, University of Texas Dell Medical School, Austin (Nemeroff); Department of Psychiatry, Beth Israel Deaconess Medical Center, Harvard Medical School, Boston (Torous); Department of Psychiatry and Behavioral Sciences, Albert Einstein School of Medicine, Bronx, N.Y. (Alpert); Department of Psychiatry and Behavioral Sciences, Stanford University, Stanford, Calif., and Veterans Affairs Palo Alto Health Care System, Palo Alto, Calif. (Rodriguez); Department of Psychiatry and Behavioral Sciences, University of Minnesota, Minneapolis (Widge)
| | - Carolyn I Rodriguez
- Department of Psychiatry and Biobehavioral Sciences, David Geffen School of Medicine, University of California, Los Angeles, and Olive View-UCLA Medical Center, Sylmar (Grzenda); Department of Psychiatry and Behavioral Neurobiology, University of Alabama at Birmingham (Kraguljac); Department of Psychiatry and Behavioral Sciences, Emory University School of Medicine, Atlanta (McDonald); Department of Psychiatry, University of Texas Dell Medical School, Austin (Nemeroff); Department of Psychiatry, Beth Israel Deaconess Medical Center, Harvard Medical School, Boston (Torous); Department of Psychiatry and Behavioral Sciences, Albert Einstein School of Medicine, Bronx, N.Y. (Alpert); Department of Psychiatry and Behavioral Sciences, Stanford University, Stanford, Calif., and Veterans Affairs Palo Alto Health Care System, Palo Alto, Calif. (Rodriguez); Department of Psychiatry and Behavioral Sciences, University of Minnesota, Minneapolis (Widge)
| | - Alik S Widge
- Department of Psychiatry and Biobehavioral Sciences, David Geffen School of Medicine, University of California, Los Angeles, and Olive View-UCLA Medical Center, Sylmar (Grzenda); Department of Psychiatry and Behavioral Neurobiology, University of Alabama at Birmingham (Kraguljac); Department of Psychiatry and Behavioral Sciences, Emory University School of Medicine, Atlanta (McDonald); Department of Psychiatry, University of Texas Dell Medical School, Austin (Nemeroff); Department of Psychiatry, Beth Israel Deaconess Medical Center, Harvard Medical School, Boston (Torous); Department of Psychiatry and Behavioral Sciences, Albert Einstein School of Medicine, Bronx, N.Y. (Alpert); Department of Psychiatry and Behavioral Sciences, Stanford University, Stanford, Calif., and Veterans Affairs Palo Alto Health Care System, Palo Alto, Calif. (Rodriguez); Department of Psychiatry and Behavioral Sciences, University of Minnesota, Minneapolis (Widge)
| |
Collapse
|
31
|
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.
Collapse
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.
| |
Collapse
|
32
|
Chien YL, Chen YC, Gau SSF. Altered cingulate structures and the associations with social awareness deficits and CNTNAP2 gene in autism spectrum disorder. NEUROIMAGE-CLINICAL 2021; 31:102729. [PMID: 34271514 PMCID: PMC8280509 DOI: 10.1016/j.nicl.2021.102729] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 01/25/2021] [Revised: 06/11/2021] [Accepted: 06/12/2021] [Indexed: 01/23/2023]
Abstract
ASD individuals showed thinner cortical thickness in bilateral cingulate subregions. The right anterior cingulate WM volume was correlated with social awareness deficit. The CNTNAP2 variant might be associated with the right middle cingulate WM volume. The CNTNAP2 might interact with ASD diagnosis and age on the cortical thickness.
Backgrounds Although evidence suggests that the activity of the anterior cingulate cortex involves social cognition, there are inconsistent findings regarding the aberrant cingulate gray matter (GM) and scanty evidence about altered cortical thickness and white matter (WM) of cingulate in individuals with autism spectrum disorder (ASD). Evidence supports the association between the genetic variants of CNTNAP2 and altered brain connectivity. This study investigated the cingulate substructure and its association with social awareness deficits and the CNTNAP2 variants in individuals with ASD and typically-developing controls (TDC). Methods We assessed 118 individuals with ASD and 122 TDC with MRI and clinical evaluation. The GM, WM volumes and cortical thickness of the cingulate gyrus were compared between ASD and TDC based on fine parcellation. Five SNPs of the CNTNAP2 linked to ASD and brain structural abnormality were genotyped, and rs2710102, rs2538991, rs2710126 passed quality control filters. Results ASD individuals showed thinner cortical thickness in bilateral cingulate subregions than TDC without significant group differences in GM and WM volumes. The WM volume of the right anterior cingulate gyrus was correlated with social awareness deficits in ASD. The CNTNAP2 variant demonstrated a main effect on the WM volumes of the right middle cingulate gyrus. Besides, the CNTNAP2 variants interacted with ASD diagnosis and age on the cortical thickness of the left anterior middle cingulate cortex. Conclusions Our findings suggest that aberrant cingulate structure in ASD might be associated with the social awareness deficits and genetic variants of the CNTNAP2. These novel findings need validation.
Collapse
Affiliation(s)
- Yi-Ling Chien
- Department of Psychiatry, National Taiwan University Hospital and College of Medicine, Taipei, Taiwan
| | - Yu-Chieh Chen
- Department of Psychiatry, National Taiwan University Hospital and College of Medicine, Taipei, Taiwan; Graduate Institute of Clinical Medicine, College of Medicine, National Taiwan University, Taipei, Taiwan
| | - Susan Shur-Fen Gau
- Department of Psychiatry, National Taiwan University Hospital and College of Medicine, Taipei, Taiwan; Graduate Institute of Clinical Medicine, College of Medicine, National Taiwan University, Taipei, Taiwan; Graduate Institute of Brain and Mind Sciences, College of Medicine, National Taiwan University, Taipei, Taiwan.
| |
Collapse
|
33
|
An KM, Ikeda T, Hirosawa T, Yaoi K, Yoshimura Y, Hasegawa C, Tanaka S, Saito DN, Kikuchi M. Decreased grey matter volumes in unaffected mothers of individuals with autism spectrum disorder reflect the broader autism endophenotype. Sci Rep 2021; 11:10001. [PMID: 33976262 PMCID: PMC8113597 DOI: 10.1038/s41598-021-89393-z] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/27/2020] [Accepted: 04/26/2021] [Indexed: 11/17/2022] Open
Abstract
Autism spectrum disorder (ASD) is a neurodevelopmental disorder with an early onset and a strong genetic origin. Unaffected relatives may present similar but subthreshold characteristics of ASD. This broader autism phenotype is especially prevalent in the parents of individuals with ASD, suggesting that it has heritable factors. Although previous studies have demonstrated brain morphometry differences in ASD, they are poorly understood in parents of individuals with ASD. Here, we estimated grey matter volume in 45 mothers of children with ASD (mASD) and 46 age-, sex-, and handedness-matched controls using whole-brain voxel-based morphometry analysis. The mASD group had smaller grey matter volume in the right middle temporal gyrus, temporoparietal junction, cerebellum, and parahippocampal gyrus compared with the control group. Furthermore, we analysed the correlations of these brain volumes with ASD behavioural characteristics using autism spectrum quotient (AQ) and systemizing quotient (SQ) scores, which measure general autistic traits and the drive to systemize. Smaller volumes in the middle temporal gyrus and temporoparietal junction correlated with higher SQ scores, and smaller volumes in the cerebellum and parahippocampal gyrus correlated with higher AQ scores. Our findings suggest that atypical grey matter volumes in mASD may represent one of the neurostructural endophenotypes of ASD.
Collapse
Affiliation(s)
- Kyung-Min An
- Research Center for Child Mental Development, Kanazawa University, 13-1 Takara-machi, Kanazawa, 920-8640, Japan.
- Division of Socio-Cognitive-Neuroscience, Department of Child Development, United Graduate School of Child Development, Osaka University, Kanazawa University, Hamamatsu University School of Medicine, Chiba University, and University of Fukui, Kanazawa, Japan.
| | - Takashi Ikeda
- Research Center for Child Mental Development, Kanazawa University, 13-1 Takara-machi, Kanazawa, 920-8640, Japan
- Division of Socio-Cognitive-Neuroscience, Department of Child Development, United Graduate School of Child Development, Osaka University, Kanazawa University, Hamamatsu University School of Medicine, Chiba University, and University of Fukui, Kanazawa, Japan
| | - Tetsu Hirosawa
- Research Center for Child Mental Development, Kanazawa University, 13-1 Takara-machi, Kanazawa, 920-8640, Japan
- Division of Socio-Cognitive-Neuroscience, Department of Child Development, United Graduate School of Child Development, Osaka University, Kanazawa University, Hamamatsu University School of Medicine, Chiba University, and University of Fukui, Kanazawa, Japan
| | - Ken Yaoi
- Research Center for Child Mental Development, Kanazawa University, 13-1 Takara-machi, Kanazawa, 920-8640, Japan
- Division of Socio-Cognitive-Neuroscience, Department of Child Development, United Graduate School of Child Development, Osaka University, Kanazawa University, Hamamatsu University School of Medicine, Chiba University, and University of Fukui, Kanazawa, Japan
| | - Yuko Yoshimura
- Research Center for Child Mental Development, Kanazawa University, 13-1 Takara-machi, Kanazawa, 920-8640, Japan
- Division of Socio-Cognitive-Neuroscience, Department of Child Development, United Graduate School of Child Development, Osaka University, Kanazawa University, Hamamatsu University School of Medicine, Chiba University, and University of Fukui, Kanazawa, Japan
- Institute of Human and Social Sciences, Kanazawa University, Kanazawa, Japan
| | - Chiaki Hasegawa
- Research Center for Child Mental Development, Kanazawa University, 13-1 Takara-machi, Kanazawa, 920-8640, Japan
| | - Sanae Tanaka
- Research Center for Child Mental Development, Kanazawa University, 13-1 Takara-machi, Kanazawa, 920-8640, Japan
- Division of Socio-Cognitive-Neuroscience, Department of Child Development, United Graduate School of Child Development, Osaka University, Kanazawa University, Hamamatsu University School of Medicine, Chiba University, and University of Fukui, Kanazawa, Japan
| | - Daisuke N Saito
- Research Center for Child Mental Development, Kanazawa University, 13-1 Takara-machi, Kanazawa, 920-8640, Japan.
- Division of Socio-Cognitive-Neuroscience, Department of Child Development, United Graduate School of Child Development, Osaka University, Kanazawa University, Hamamatsu University School of Medicine, Chiba University, and University of Fukui, Kanazawa, Japan.
| | - Mitsuru Kikuchi
- Research Center for Child Mental Development, Kanazawa University, 13-1 Takara-machi, Kanazawa, 920-8640, Japan
- Division of Socio-Cognitive-Neuroscience, Department of Child Development, United Graduate School of Child Development, Osaka University, Kanazawa University, Hamamatsu University School of Medicine, Chiba University, and University of Fukui, Kanazawa, Japan
- Department of Psychiatry and Behavioral Science, Kanazawa University, Kanazawa, Japan
| |
Collapse
|
34
|
He Q, Wang Q, Wu Y, Yi L, Wei K. Automatic classification of children with autism spectrum disorder by using a computerized visual-orienting task. Psych J 2021; 10:550-565. [PMID: 33847077 DOI: 10.1002/pchj.447] [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: 02/15/2020] [Revised: 12/04/2020] [Accepted: 03/09/2021] [Indexed: 11/09/2022]
Abstract
Early screening and diagnosis of autism spectrum disorder (ASD) primarily rely on behavioral observations by qualified clinicians whose decision process can benefit from the combination of machine learning algorithms and sensor data. We designed a computerized visual-orienting task with gaze-related or non-gaze-related directional cues, which triggered participants' gaze-following behavior. Based on their eye-movement data registered by an eye tracker, we applied the machine learning algorithms to classify high-functioning children with ASD (HFA), low-functioning children with ASD (LFA), and typically developing children (TD). We found that TD children had higher success rates in obtaining rewards than HFA children, and HFA children had higher rates than LFA children. Based on raw eye-tracking data, our machine learning algorithm could classify the three groups with an accuracy of 81.1% and relatively high sensitivity and specificity. Classification became worse if only data from the gaze or nongaze conditions were used, suggesting that "less-social" directional cues also carry useful information for distinguishing these groups. Our findings not only provide insights about visual-orienting deficits among children with ASD but also demonstrate the promise of combining classical behavioral paradigms with machine learning algorithms for aiding the screening for individuals with ASD.
Collapse
Affiliation(s)
- Qiao He
- Peking-Tsinghua Center for Life Sciences, Academy for Advanced Interdisciplinary Studies, Peking University, Beijing, China
| | - Qiandong Wang
- Beijing Key Laboratory of Applied Experimental Psychology, National Demonstration Center for Experimental Psychology Education (Beijing Normal University), Faculty of Psychology, Beijing Normal University, Beijing, China
| | - Yaxue Wu
- School of Psychological and Cognitive Sciences, Peking University, Beijing, China.,Beijing Key Laboratory of Behavior and Mental Health, Peking University, Beijing, China
| | - Li Yi
- School of Psychological and Cognitive Sciences, Peking University, Beijing, China.,Beijing Key Laboratory of Behavior and Mental Health, Peking University, Beijing, China
| | - Kunlin Wei
- School of Psychological and Cognitive Sciences, Peking University, Beijing, China.,Beijing Key Laboratory of Behavior and Mental Health, Peking University, Beijing, China
| |
Collapse
|
35
|
Eslami T, Almuqhim F, Raiker JS, Saeed F. Machine Learning Methods for Diagnosing Autism Spectrum Disorder and Attention- Deficit/Hyperactivity Disorder Using Functional and Structural MRI: A Survey. Front Neuroinform 2021; 14:575999. [PMID: 33551784 PMCID: PMC7855595 DOI: 10.3389/fninf.2020.575999] [Citation(s) in RCA: 20] [Impact Index Per Article: 6.7] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/24/2020] [Accepted: 12/07/2020] [Indexed: 12/15/2022] Open
Abstract
Here we summarize recent progress in machine learning model for diagnosis of Autism Spectrum Disorder (ASD) and Attention-deficit/Hyperactivity Disorder (ADHD). We outline and describe the machine-learning, especially deep-learning, techniques that are suitable for addressing research questions in this domain, pitfalls of the available methods, as well as future directions for the field. We envision a future where the diagnosis of ASD, ADHD, and other mental disorders is accomplished, and quantified using imaging techniques, such as MRI, and machine-learning models.
Collapse
Affiliation(s)
- Taban Eslami
- Department of Computer Science, Western Michigan University, Kalamazoo, MI, United States
| | - Fahad Almuqhim
- School of Computing and Information Sciences, Florida International University, Miami, FL, United States
| | - Joseph S. Raiker
- Department of Psychology, Florida International University, Miami, FL, United States
| | - Fahad Saeed
- School of Computing and Information Sciences, Florida International University, Miami, FL, United States
| |
Collapse
|
36
|
Fu Y, Zhang J, Li Y, Shi J, Zou Y, Guo H, Li Y, Yao Z, Wang Y, Hu B. A novel pipeline leveraging surface-based features of small subcortical structures to classify individuals with autism spectrum disorder. Prog Neuropsychopharmacol Biol Psychiatry 2021; 104:109989. [PMID: 32512131 PMCID: PMC9632410 DOI: 10.1016/j.pnpbp.2020.109989] [Citation(s) in RCA: 15] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/10/2020] [Revised: 05/19/2020] [Accepted: 05/30/2020] [Indexed: 10/24/2022]
Abstract
Autism spectrum disorder (ASD) is accompanied with widespread impairment in social-emotional functioning. Classification of ASD using sensitive morphological features derived from structural magnetic resonance imaging (MRI) of the brain may help us to better understand ASD-related mechanisms and improve related automatic diagnosis. Previous studies using T1 MRI scans in large heterogeneous ABIDE dataset with typical development (TD) controls reported poor classification accuracies (around 60%). This may because they only considered surface-based morphometry (SBM) as scalar estimates (such as cortical thickness and surface area) and ignored the neighboring intrinsic geometry information among features. In recent years, the shape-related SBM achieves great success in discovering the disease burden and progression of other brain diseases. However, when focusing on local geometry information, its high dimensionality requires careful treatment in its application to machine learning. To address the above challenges, we propose a novel pipeline for ASD classification, which mainly includes the generation of surface-based features, patch-based surface sparse coding and dictionary learning, Max-pooling and ensemble classifiers based on adaptive optimizers. The proposed pipeline may leverage the sensitivity of brain surface morphometry statistics and the efficiency of sparse coding and Max-pooling. By introducing only the surface features of bilateral hippocampus that derived from 364 male subjects with ASD and 381 age-matched TD males, this pipeline outperformed five recent MRI-based ASD classification studies with >80% accuracy in discriminating individuals with ASD from TD controls. Our results suggest shape-related SBM features may further boost the classification performance of MRI between ASD and TD.
Collapse
Affiliation(s)
- Yu Fu
- School of Information Science and Engineering, Lanzhou University, Lanzhou, Gansu Province, China
| | - Jie Zhang
- School of Computing, Informatics, and Decision Systems Engineering, Arizona State University, Tempe, AZ, USA
| | - Yuan Li
- School of Information Science and Engineering, Shandong Normal University, Jinan, Shandong Province, China
| | - Jie Shi
- School of Information Science and Engineering, Lanzhou University, Lanzhou, Gansu Province, China
| | - Ying Zou
- School of Information Science and Engineering, Lanzhou University, Lanzhou, Gansu Province, China
| | - Hanning Guo
- School of Information Science and Engineering, Lanzhou University, Lanzhou, Gansu Province, China
| | - Yongchao Li
- School of Information Science and Engineering, Lanzhou University, Lanzhou, Gansu Province, China
| | - Zhijun Yao
- School of Information Science and Engineering, Lanzhou University, Lanzhou, Gansu Province, China.
| | - Yalin Wang
- School of Computing, Informatics, and Decision Systems Engineering, Arizona State University, Tempe, AZ, USA.
| | - Bin Hu
- School of Information Science and Engineering, Lanzhou University, Lanzhou, Gansu Province, China; Gansu Provincial Key Laboratory of Wearable Computing, School of Information Science and Engineering, Lanzhou University, Lanzhou, Gansu Province, China; CAS Center for Excellence in Brain Science and Intelligence Technology, Shanghai Institutes for Biological Sciences, Chinese Academy of Sciences, Shanghai, China; Beijing Institute for Brain Disorders, Capital Medical University, Beijing, China.
| |
Collapse
|
37
|
Najafpour Z, Fatemi A, Goudarzi Z, Goudarzi R, Shayanfard K, Noorizadeh F. Cost-effectiveness of neuroimaging technologies in management of psychiatric and insomnia disorders: A meta-analysis and prospective cost analysis. J Neuroradiol 2021; 48:348-358. [PMID: 33383065 DOI: 10.1016/j.neurad.2020.12.003] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/23/2020] [Revised: 12/12/2020] [Accepted: 12/15/2020] [Indexed: 01/10/2023]
Abstract
BACKGROUND The optimal diagnostic strategy for patients with psychiatric and insomnia disorders has not been established yet. PURPOSE The purpose of this study was to perform cost-effectiveness analysis of six neuroimaging technologies in diagnosis of patients with psychiatric and insomnia disorders. METHODS An economic evaluation study was conducted in three parts, including a systematic review for determining diagnostic accuracy, a descriptive cross-sectional study with Activity-Based Costing (ABC) technique for tracing resource consumption, and a cost-effectiveness analysis using a short-term decision-analytic model. RESULTS In the first phase, 93 diagnostic accuracy studies were included in the systematic review. The accumulated results (meta-analysis) showed that the highest diagnostic accuracy for psychiatric and insomnia disorders was attributed to PET (sensitivity of 90% and specificity of 80%) and MRI (sensitivity of 76% and specificity of 78%) respectively. In the second phase of the study, we calculated the cost of each technology. The results showed that MRI has the lowest cost. Based on the results in the model of cost-effectiveness sMRI ($ 50.08 per accurate diagnosis) and MRI ($ 58.54 per accurate diagnosis) were more cost-effective neuroimaging technologies. CONCLUSION In psychiatric disorders, no single strategy was characterized by both low cost and high accuracy. However, MRI and PET scan had lower cost and higher accuracy for psychiatric disorders, respectively. MRI was the least costly with the highest diagnostic accuracy in insomnia disorders. Based on our model, sMRI in psychiatric disorders and MRI in insomnia disorders were the most cost-effective technologies.
Collapse
Affiliation(s)
- Zhila Najafpour
- Department of Health Care Management, School of Public Health, Ahvaz Jundishapur University of Medical Sciences, Ahvaz, Iran.
| | - Asieh Fatemi
- Dpartment of Pharmacoeconomics and Pharmaceutical Administration, Faculty of Pharmacy, Tehran University of Medical Sciences, Faculty of Paramedical sciences, Rafsanjan University of Medical Sciences, Iran.
| | - Zahra Goudarzi
- Department of Pharmacoeconomics and Pharmaceutical Administration, Faculty of Pharmacy, Tehran University of Medical Sciences, Tehran, Iran.
| | - Reza Goudarzi
- Health Services Management Research Center, Institute for Futures Studies in Health, Kerman University of Medical Sciences, Kerman, Iran.
| | | | - Farsad Noorizadeh
- Basir Eye Health Research Center, Exceptional Talents Development Center, Tehran University of Medical Sciences, Tehran, Iran.
| |
Collapse
|
38
|
Sarovic D, Hadjikhani N, Schneiderman J, Lundström S, Gillberg C. Autism classified by magnetic resonance imaging: A pilot study of a potential diagnostic tool. Int J Methods Psychiatr Res 2020; 29:1-18. [PMID: 32945591 PMCID: PMC7723195 DOI: 10.1002/mpr.1846] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/04/2018] [Revised: 06/10/2020] [Accepted: 06/11/2020] [Indexed: 12/25/2022] Open
Abstract
OBJECTIVES Individual anatomical biomarkers have limited power for the classification of autism. The present study introduces a multivariate classification approach using structural magnetic resonance imaging data from individuals with and without autism. METHODS The classifier utilizes z-normalization, parameter weighting, and interindividual comparison on brain segmentation data, for estimation of an individual summed total index (TI). The TI indicates whether the gross morphological pattern of each individual's brain is in the direction of cases or controls. RESULTS Morphometric analysis found significant differences within subcortical gray matter structures and limbic areas. There was no significant difference in total brain volume. A case-control pilot-study of TIs in normally intelligent individuals with autism (24) and without (21) yielded a maximal accuracy of 78.9% following cross-validation. It showed a high accuracy compared with machine learning methods when tested on the same dataset. The TI correlated well with the autism quotient (R = 0.51) across groups. CONCLUSION These results are on par with studies on autism using machine learning. The main contributions are its transparency and simplicity. The possibility of including additional neuroimaging data further increases the potential of the classifier as a diagnostic aid for neuropsychiatric disorders, as well as a research tool for neuroscientific investigations.
Collapse
Affiliation(s)
- Darko Sarovic
- Gillberg Neuropsychiatry Centre, Department of Psychiatry and Neurochemistry, Institute of Neuroscience and Physiology, Sahlgrenska Academy, University of Gothenburg, Gothenburg, Sweden.,MedTech West, Gothenburg, Sweden
| | - Nouchine Hadjikhani
- Gillberg Neuropsychiatry Centre, Department of Psychiatry and Neurochemistry, Institute of Neuroscience and Physiology, Sahlgrenska Academy, University of Gothenburg, Gothenburg, Sweden.,Department of Radiology, Athinoula A. Martinos Center for Biomedical Imaging, Harvard University, Charlestown, Massachusetts, USA
| | - Justin Schneiderman
- MedTech West, Gothenburg, Sweden.,Department of Clinical Neurophysiology, Institute of Neuroscience and Physiology, Sahlgrenska Academy, University of Gothenburg, Gothenburg, Sweden
| | - Sebastian Lundström
- Gillberg Neuropsychiatry Centre, Department of Psychiatry and Neurochemistry, Institute of Neuroscience and Physiology, Sahlgrenska Academy, University of Gothenburg, Gothenburg, Sweden
| | - Christopher Gillberg
- Gillberg Neuropsychiatry Centre, Department of Psychiatry and Neurochemistry, Institute of Neuroscience and Physiology, Sahlgrenska Academy, University of Gothenburg, Gothenburg, Sweden.,Institute of Health & Wellbeing, University of Glasgow, Glasgow, Scotland, UK
| |
Collapse
|
39
|
Ma X, Wang XH, Li L. Identifying individuals with autism spectrum disorder based on the principal components of whole-brain phase synchrony. Neurosci Lett 2020; 742:135519. [PMID: 33246027 DOI: 10.1016/j.neulet.2020.135519] [Citation(s) in RCA: 9] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/08/2020] [Revised: 11/03/2020] [Accepted: 11/19/2020] [Indexed: 11/29/2022]
Abstract
Autism spectrum disorder (ASD) is a brain disorder that develops during an early stage of childhood. Previous neuroimaging-based diagnostic models for ASD were based on static functional connectivity (FC). The nonlinear complexity of brain connectivity remains unexplored for ASD diagnosis. This study aimed to build intelligent discriminative models for ASD based on phase synchrony (PS). To this end, data from 49 patients with ASD and 41 healthy controls were obtained from the Autism Brain Imaging Data Exchange (ABIDE) project. PS between brain regions was determined using Hilbert transform. Principal component analysis (PCA) and support vector machines (SVMs) were used to build the discriminative models. PS-based models (AUC = 0.81) outperformed static FC-based models (AUC = 0.71). Furthermore, embedded functional biomarkers were discovered. Moreover, significant correlations were found between PCA-PS and the clinical severity of ASD. Together, intelligent discriminative models based on PS were established for ASD identification. The performance of the diagnostic models suggested the potential benefits of PS for clinical applications. The discriminative patterns indicated that PCA-PS features could be additional biomarkers for ASD research. Furthermore, the significant relationships between the PCA-PS features and clinical scores implied their potential use for personalized medication strategies.
Collapse
Affiliation(s)
- Xueke Ma
- Institute of Biomedical Engineering and Instrumentation, Hangzhou Dianzi University, Hangzhou, 310018, China
| | - Xun-Heng Wang
- Institute of Biomedical Engineering and Instrumentation, Hangzhou Dianzi University, Hangzhou, 310018, China
| | - Lihua Li
- Institute of Biomedical Engineering and Instrumentation, Hangzhou Dianzi University, Hangzhou, 310018, China.
| |
Collapse
|
40
|
Liu X, Bautista J, Liu E, Zikopoulos B. Imbalance of laminar-specific excitatory and inhibitory circuits of the orbitofrontal cortex in autism. Mol Autism 2020; 11:83. [PMID: 33081829 PMCID: PMC7574354 DOI: 10.1186/s13229-020-00390-x] [Citation(s) in RCA: 16] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/19/2020] [Accepted: 10/06/2020] [Indexed: 12/11/2022] Open
Abstract
BACKGROUND The human orbitofrontal cortex (OFC) is involved in assessing the emotional significance of events and stimuli, emotion-based learning, allocation of attentional resources, and social cognition. Little is known about the structure, connectivity and excitatory/inhibitory circuit interactions underlying these diverse functions in human OFC, as well as how the circuit is disrupted in individuals with autism spectrum disorder (ASD). METHODS We used post-mortem brain tissue from neurotypical adults and individuals with ASD. We examined the morphology and distribution of myelinated axons across cortical layers in OFC, at the single axon level, as a proxy of excitatory pathways. In the same regions, we also examined the laminar distribution of all neurons and neurochemically- and functionally-distinct inhibitory neurons that express the calcium-binding proteins parvalbumin (PV), calbindin (CB), and calretinin (CR). RESULTS We found that the density of myelinated axons increased consistently towards layer 6, while the average axon diameter did not change significantly across layers in both groups. However, both the density and diameter of myelinated axons were significantly lower in the ASD group compared with the Control group. The distribution pattern and density of the three major types of inhibitory neurons was comparable between groups, but there was a significant reduction in the density of excitatory neurons across OFC layers in ASD. LIMITATIONS This study is limited by the availability of human post-mortem tissue optimally processed for high-resolution microscopy and immunolabeling, especially from individuals with ASD. CONCLUSIONS The balance between excitation and inhibition in OFC is at the core of its function, assessing and integrating emotional and social cues with internal states and external inputs. Our preliminary results provide evidence for laminar-specific changes in the ratio of excitation/inhibition in OFC of adults with ASD, with an overall weakening and likely disorganization of excitatory signals and a relative strengthening of local inhibition. These changes likely underlie pathology of major OFC communications with limbic or other cortices and the amygdala in individuals with ASD, and may provide the anatomic basis for disrupted transmission of signals for social interactions and emotions in autism.
Collapse
Affiliation(s)
- Xuefeng Liu
- Human Systems Neuroscience Laboratory, Department of Health Sciences, Boston University, 635 Commonwealth Ave., Room 401D, Boston, MA, 02215, USA
| | - Julied Bautista
- Human Systems Neuroscience Laboratory, Department of Health Sciences, Boston University, 635 Commonwealth Ave., Room 401D, Boston, MA, 02215, USA
| | - Edward Liu
- Human Systems Neuroscience Laboratory, Department of Health Sciences, Boston University, 635 Commonwealth Ave., Room 401D, Boston, MA, 02215, USA
| | - Basilis Zikopoulos
- Human Systems Neuroscience Laboratory, Department of Health Sciences, Boston University, 635 Commonwealth Ave., Room 401D, Boston, MA, 02215, USA. .,Department of Anatomy and Neurobiology, Boston University School of Medicine, Boston, MA, USA. .,Graduate Program in Neuroscience, Boston University, Boston, MA, USA.
| |
Collapse
|
41
|
Jung M, Mizuno Y, Fujisawa TX, Takiguchi S, Kong J, Kosaka H, Tomoda A. The Effects of COMT Polymorphism on Cortical Thickness and Surface Area Abnormalities in Children with ADHD. Cereb Cortex 2020; 29:3902-3911. [PMID: 30508034 DOI: 10.1093/cercor/bhy269] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/07/2018] [Revised: 09/21/2018] [Indexed: 11/12/2022] Open
Abstract
The catechol-O-methyltransferase (COMT) gene is associated with frontal cortex development and the pathophysiology of attention-deficit/hyperactivity disorder (ADHD). However, how the COMT gene impacts brain structure and behavior in ADHD remains unknown. In the present study, we identify the effect of COMT on cortical thickness and surface area in children with ADHD and children with typically developing (TD) using a machine learning approach. In a sample of 39 children with ADHD and 34 age- and IQ-matched TD children, we found that cortical thickness and surface area differences were predominantly observed in the frontal cortex. Furthermore, a path analysis revealed that a COMT genotype affected abnormal development of the frontal cortex in terms of both cortical thickness and surface area and was associated with working memory changes in children with ADHD. Our study confirms that the role of COMT in ADHD is not restricted to the development of behavior but may also affect the cortical thickness and surface area. Thus, our findings may help to improve the understanding of the neuroanatomic basis for the relationship between the COMT genotype and ADHD pathogenesis.
Collapse
Affiliation(s)
- Minyoung Jung
- Research Center for Child Mental Development, University of Fukui, Eiheiji, Fukui, Japan
| | - Yoshifumi Mizuno
- Department of Child and Adolescent Psychological Medicine, University of Fukui Hospital, Fukui, Japan
| | - Takashi X Fujisawa
- Research Center for Child Mental Development, University of Fukui, Eiheiji, Fukui, Japan
| | - Shinichiro Takiguchi
- Department of Child and Adolescent Psychological Medicine, University of Fukui Hospital, Fukui, Japan
| | - Jian Kong
- Department of Psychiatry, Massachusetts General Hospital, Harvard Medical School, Charlestown, MA, USA
| | - Hirotaka Kosaka
- Research Center for Child Mental Development, University of Fukui, Eiheiji, Fukui, Japan.,Department of Child and Adolescent Psychological Medicine, University of Fukui Hospital, Fukui, Japan.,Department of Neuropsychiatry, University of Fukui, University of Fukui, Eiheiji, Fukui, Japan
| | - Akemi Tomoda
- Research Center for Child Mental Development, University of Fukui, Eiheiji, Fukui, Japan.,Department of Child and Adolescent Psychological Medicine, University of Fukui Hospital, Fukui, Japan
| |
Collapse
|
42
|
Bethlehem RAI, Seidlitz J, Romero-Garcia R, Trakoshis S, Dumas G, Lombardo MV. A normative modelling approach reveals age-atypical cortical thickness in a subgroup of males with autism spectrum disorder. Commun Biol 2020; 3:486. [PMID: 32887930 PMCID: PMC7474067 DOI: 10.1038/s42003-020-01212-9] [Citation(s) in RCA: 47] [Impact Index Per Article: 11.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/16/2020] [Accepted: 08/05/2020] [Indexed: 12/27/2022] Open
Abstract
Understanding heterogeneity is an important goal on the path to precision medicine for autism spectrum disorders (ASD). We examined how cortical thickness (CT) in ASD can be parameterized as an individualized metric of atypicality relative to typically-developing (TD) age-related norms. Across a large sample (n = 870 per group) and wide age range (5-40 years), we applied normative modelling resulting in individualized whole-brain maps of age-related CT atypicality in ASD and isolating a small subgroup with highly age-atypical CT. Age-normed CT scores also highlights on-average differentiation, and associations with behavioural symptomatology that is separate from insights gleaned from traditional case-control approaches. This work showcases an individualized approach for understanding ASD heterogeneity that could potentially further prioritize work on a subset of individuals with cortical pathophysiology represented in age-related CT atypicality. Only a small subset of ASD individuals are actually highly atypical relative to age-norms. driving small on-average case-control differences.
Collapse
Affiliation(s)
- Richard A I Bethlehem
- Brain Mapping Unit, Department of Psychiatry, University of Cambridge, Cambridge, CB2 0SZ, UK. .,Autism Research Centre, Department of Psychiatry, University of Cambridge, Cambridge, CB2 8AH, UK.
| | - Jakob Seidlitz
- Brain Mapping Unit, Department of Psychiatry, University of Cambridge, Cambridge, CB2 0SZ, UK.,Department of Child and Adolescent Psychiatry and Behavioral Science, Children's Hospital of Philadelphia, Philadelphia, PA, 19104, USA
| | - Rafael Romero-Garcia
- Brain Mapping Unit, Department of Psychiatry, University of Cambridge, Cambridge, CB2 0SZ, UK
| | - Stavros Trakoshis
- Laboratory for Autism and Neurodevelopmental Disorders, Center for Neuroscience and Cognitive Systems @UniTn, Istituto Italiano di Tecnologia, Rovereto, Italy.,Department of Psychology, University of Cyprus, Nicosia, Cyprus
| | - Guillaume Dumas
- Human Genetics and Cognitive Functions Unit, Institut Pasteur, Paris, France.,CNRS UMR3571 Genes, Synapses and Cognition, Institut Pasteur, Paris, France.,Human Genetics and Cognitive Functions Unit, University Paris Diderot, Sorbonne Paris Cité, Paris, France
| | - Michael V Lombardo
- Autism Research Centre, Department of Psychiatry, University of Cambridge, Cambridge, CB2 8AH, UK.,Laboratory for Autism and Neurodevelopmental Disorders, Center for Neuroscience and Cognitive Systems @UniTn, Istituto Italiano di Tecnologia, Rovereto, Italy
| |
Collapse
|
43
|
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: 55] [Impact Index Per Article: 13.8] [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.
Collapse
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
| |
Collapse
|
44
|
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: 58] [Impact Index Per Article: 14.5] [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.
Collapse
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.
| |
Collapse
|
45
|
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: 78] [Impact Index Per Article: 19.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/21/2019] [Revised: 04/06/2020] [Accepted: 04/06/2020] [Indexed: 01/10/2023] Open
Abstract
Neuroimaging-based approaches have been extensively applied to study mental illness in recent years and have deepened our understanding of both cognitively healthy and disordered brain structure and function. Recent advancements in machine learning techniques have shown promising outcomes for individualized prediction and characterization of patients with psychiatric disorders. Studies have utilized features from a variety of neuroimaging modalities, including structural, functional, and diffusion magnetic resonance imaging data, as well as jointly estimated features from multiple modalities, to assess patients with heterogeneous mental disorders, such as schizophrenia and autism. We use the term "predictome" to describe the use of multivariate brain network features from one or more neuroimaging modalities to predict mental illness. In the predictome, multiple brain network-based features (either from the same modality or multiple modalities) are incorporated into a predictive model to jointly estimate features that are unique to a disorder and predict subjects accordingly. To date, more than 650 studies have been published on subject-level prediction focusing on psychiatric disorders. We have surveyed about 250 studies including schizophrenia, major depression, bipolar disorder, autism spectrum disorder, attention-deficit hyperactivity disorder, obsessive-compulsive disorder, social anxiety disorder, posttraumatic stress disorder, and substance dependence. In this review, we present a comprehensive review of recent neuroimaging-based predictomic approaches, current trends, and common shortcomings and share our vision for future directions.
Collapse
Affiliation(s)
- Barnaly Rashid
- Department of PsychiatryHarvard Medical SchoolBostonMassachusettsUSA
| | - Vince Calhoun
- Tri‐Institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS)Georgia State University, Georgia Institute of Technology, and Emory UniversityAtlantaGeorgiaUSA
| |
Collapse
|
46
|
Hashem S, Nisar S, Bhat AA, Yadav SK, Azeem MW, Bagga P, Fakhro K, Reddy R, Frenneaux MP, Haris M. Genetics of structural and functional brain changes in autism spectrum disorder. Transl Psychiatry 2020; 10:229. [PMID: 32661244 PMCID: PMC7359361 DOI: 10.1038/s41398-020-00921-3] [Citation(s) in RCA: 60] [Impact Index Per Article: 15.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/20/2020] [Revised: 06/05/2020] [Accepted: 06/09/2020] [Indexed: 12/21/2022] Open
Abstract
Autism spectrum disorder (ASD) is a neurological and developmental disorder characterized by social impairment and restricted interactive and communicative behaviors. It may occur as an isolated disorder or in the context of other neurological, psychiatric, developmental, and genetic disorders. Due to rapid developments in genomics and imaging technologies, imaging genetics studies of ASD have evolved in the last few years. Increased risk for ASD diagnosis is found to be related to many specific single-nucleotide polymorphisms, and the study of genetic mechanisms and noninvasive imaging has opened various approaches that can help diagnose ASD at the nascent level. Identifying risk genes related to structural and functional changes in the brain of ASD patients provide a better understanding of the disease's neuropsychiatry and can help identify targets for therapeutic intervention that could be useful for the clinical management of ASD patients.
Collapse
Affiliation(s)
- Sheema Hashem
- Functional and Molecular Imaging Laboratory, Sidra Medicine, Doha, Qatar
| | - Sabah Nisar
- Functional and Molecular Imaging Laboratory, Sidra Medicine, Doha, Qatar
| | - Ajaz A Bhat
- Functional and Molecular Imaging Laboratory, Sidra Medicine, Doha, Qatar
| | | | | | - Puneet Bagga
- Center for Magnetic Resonance and Optical Imaging, Department of Radiology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, 19104, USA
| | - Khalid Fakhro
- Department of Human Genetics, Sidra Medicine, Doha, Qatar
- Department of Genetic Medicine, Weill Cornell Medical College, Doha, Qatar
| | - Ravinder Reddy
- Center for Magnetic Resonance and Optical Imaging, Department of Radiology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, 19104, USA
| | | | - Mohammad Haris
- Functional and Molecular Imaging Laboratory, Sidra Medicine, Doha, Qatar.
- Laboratory Animal Research Center, Qatar University, Doha, Qatar.
| |
Collapse
|
47
|
Nunes AS, Vakorin VA, Kozhemiako N, Peatfield N, Ribary U, Doesburg SM. Atypical age-related changes in cortical thickness in autism spectrum disorder. Sci Rep 2020; 10:11067. [PMID: 32632150 PMCID: PMC7338512 DOI: 10.1038/s41598-020-67507-3] [Citation(s) in RCA: 23] [Impact Index Per Article: 5.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/02/2019] [Accepted: 06/08/2020] [Indexed: 01/17/2023] Open
Abstract
Recent longitudinal neuroimaging and neurophysiological studies have shown that tracking relative age-related changes in neural signals, rather than a static snapshot of a neural measure, could offer higher sensitivity for discriminating typically developing (TD) individuals from those with autism spectrum disorder (ASD). It is not clear, however, which aspects of age-related changes (trajectories) would be optimal for identifying atypical brain development in ASD. Using a large cross-sectional data set (Autism Brain Imaging Data Exchange [ABIDE] repository; releases I and II), we aimed to explore age-related changes in cortical thickness (CT) in TD and ASD populations (age range 6–30 years old). Cortical thickness was estimated from T1-weighted MRI images at three scales of spatial coarseness (three parcellations with different numbers of regions of interest). For each parcellation, three polynomial models of age-related changes in CT were tested. Specifically, to characterize alterations in CT trajectories, we compared the linear slope, curvature, and aberrancy of CT trajectories across experimental groups, which was estimated using linear, quadratic, and cubic polynomial models, respectively. Also, we explored associations between age-related changes with ASD symptomatology quantified as the Autism Diagnostic Observation Schedule (ADOS) scores. While no overall group differences in cortical thickness were observed across the entire age range, ASD and TD populations were different in terms of age-related changes, which were located primarily in frontal and tempo-parietal areas. These atypical age-related changes were also associated with ADOS scores in the ASD group and used to predict ASD from TD development. These results indicate that the curvature is the most reliable feature for localizing brain areas developmentally atypical in ASD with a more pronounced effect with symptomatology and is the most sensitive in predicting ASD development.
Collapse
Affiliation(s)
- Adonay S Nunes
- Department of Biomedical Physiology and Kinesiology, Simon Fraser University, 8888 University Dr, Burnaby, BC, V5A 1S6, Canada.
| | - Vasily A Vakorin
- Department of Biomedical Physiology and Kinesiology, Simon Fraser University, 8888 University Dr, Burnaby, BC, V5A 1S6, Canada.,Behavioral & Cognitive Neuroscience Institute, Simon Fraser University, Burnaby, Canada
| | - Nataliia Kozhemiako
- Department of Biomedical Physiology and Kinesiology, Simon Fraser University, 8888 University Dr, Burnaby, BC, V5A 1S6, Canada
| | - Nicholas Peatfield
- Department of Biomedical Physiology and Kinesiology, Simon Fraser University, 8888 University Dr, Burnaby, BC, V5A 1S6, Canada
| | - Urs Ribary
- Behavioral & Cognitive Neuroscience Institute, Simon Fraser University, Burnaby, Canada.,Department Pediatrics and Psychiatry, University of British Columbia, Vancouver, Canada.,B.C. Children's Hospital Research Institute, Vancouver, Canada.,Department Psychology, Simon Fraser University, Burnaby, Canada
| | - Sam M Doesburg
- Department of Biomedical Physiology and Kinesiology, Simon Fraser University, 8888 University Dr, Burnaby, BC, V5A 1S6, Canada.,Behavioral & Cognitive Neuroscience Institute, Simon Fraser University, Burnaby, Canada
| |
Collapse
|
48
|
Vargason T, Grivas G, Hollowood-Jones KL, Hahn J. Towards a Multivariate Biomarker-Based Diagnosis of Autism Spectrum Disorder: Review and Discussion of Recent Advancements. Semin Pediatr Neurol 2020; 34:100803. [PMID: 32446437 PMCID: PMC7248126 DOI: 10.1016/j.spen.2020.100803] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/12/2022]
Abstract
An ever-evolving understanding of autism spectrum disorder (ASD) pathophysiology necessitates that diagnostic standards also evolve from being observation-based to include quantifiable clinical measurements. The multisystem nature of ASD motivates the use of multivariate methods of statistical analysis over common univariate approaches for discovering clinical biomarkers relevant to this goal. In addition to characterization of important behavioral patterns for improving current diagnostic instruments, multivariate analyses to date have allowed for thorough investigation of neuroimaging-based, genetic, and metabolic abnormalities in individuals with ASD. This review highlights current research using multivariate statistical analyses to quantify the value of these behavioral and physiological markers for ASD diagnosis. A detailed discussion of a blood-based diagnostic test for ASD using specific metabolite concentrations is also provided. The advancement of ASD biomarker research promises to provide earlier and more accurate diagnoses of the disorder.
Collapse
Affiliation(s)
- Troy Vargason
- Department of Biomedical Engineering, Rensselaer Polytechnic Institute, Troy, NY; Center for Biotechnology and Interdisciplinary Studies, Rensselaer Polytechnic Institute, Troy, NY
| | - Genevieve Grivas
- Department of Biomedical Engineering, Rensselaer Polytechnic Institute, Troy, NY; Center for Biotechnology and Interdisciplinary Studies, Rensselaer Polytechnic Institute, Troy, NY
| | - Kathryn L Hollowood-Jones
- Department of Biomedical Engineering, Rensselaer Polytechnic Institute, Troy, NY; Center for Biotechnology and Interdisciplinary Studies, Rensselaer Polytechnic Institute, Troy, NY
| | - Juergen Hahn
- Department of Biomedical Engineering, Rensselaer Polytechnic Institute, Troy, NY; Center for Biotechnology and Interdisciplinary Studies, Rensselaer Polytechnic Institute, Troy, NY; Department of Chemical and Biological Engineering, Rensselaer Polytechnic Institute, Troy, NY.
| |
Collapse
|
49
|
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.
Collapse
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
| |
Collapse
|
50
|
Moon SJ, Hwang J, Kana R, Torous J, Kim JW. Accuracy of Machine Learning Algorithms for the Diagnosis of Autism Spectrum Disorder: Systematic Review and Meta-Analysis of Brain Magnetic Resonance Imaging Studies. JMIR Ment Health 2019; 6:e14108. [PMID: 31562756 PMCID: PMC6942187 DOI: 10.2196/14108] [Citation(s) in RCA: 26] [Impact Index Per Article: 5.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/22/2019] [Revised: 09/10/2019] [Accepted: 09/24/2019] [Indexed: 01/19/2023] Open
Abstract
BACKGROUND In the recent years, machine learning algorithms have been more widely and increasingly applied in biomedical fields. In particular, their application has been drawing more attention in the field of psychiatry, for instance, as diagnostic tests/tools for autism spectrum disorder (ASD). However, given their complexity and potential clinical implications, there is an ongoing need for further research on their accuracy. OBJECTIVE This study aimed to perform a systematic review and meta-analysis to summarize the available evidence for the accuracy of machine learning algorithms in diagnosing ASD. METHODS The following databases were searched on November 28, 2018: MEDLINE, EMBASE, CINAHL Complete (with Open Dissertations), PsycINFO, and Institute of Electrical and Electronics Engineers Xplore Digital Library. Studies that used a machine learning algorithm partially or fully for distinguishing individuals with ASD from control subjects and provided accuracy measures were included in our analysis. The bivariate random effects model was applied to the pooled data in a meta-analysis. A subgroup analysis was used to investigate and resolve the source of heterogeneity between studies. True-positive, false-positive, false-negative, and true-negative values from individual studies were used to calculate the pooled sensitivity and specificity values, draw Summary Receiver Operating Characteristics curves, and obtain the area under the curve (AUC) and partial AUC (pAUC). RESULTS A total of 43 studies were included for the final analysis, of which a meta-analysis was performed on 40 studies (53 samples with 12,128 participants). A structural magnetic resonance imaging (sMRI) subgroup meta-analysis (12 samples with 1776 participants) showed a sensitivity of 0.83 (95% CI 0.76-0.89), a specificity of 0.84 (95% CI 0.74-0.91), and AUC/pAUC of 0.90/0.83. A functional magnetic resonance imaging/deep neural network subgroup meta-analysis (5 samples with 1345 participants) showed a sensitivity of 0.69 (95% CI 0.62-0.75), specificity of 0.66 (95% CI 0.61-0.70), and AUC/pAUC of 0.71/0.67. CONCLUSIONS The accuracy of machine learning algorithms for diagnosis of ASD was considered acceptable by few accuracy measures only in cases of sMRI use; however, given the many limitations indicated in our study, further well-designed studies are warranted to extend the potential use of machine learning algorithms to clinical settings. TRIAL REGISTRATION PROSPERO CRD42018117779; https://www.crd.york.ac.uk/prospero/display_record.php?RecordID=117779.
Collapse
Affiliation(s)
- Sun Jae Moon
- Ewha Womans University Mokdong Hospital, Ewha Womans University Medical Center, Seoul, Republic of Korea
| | - Jinseub Hwang
- Department of Computer Science and Statistics, Daegu University, Gyeongsangbuk-do, Republic of Korea
| | - Rajesh Kana
- Department of Psychology, University of Alabama at Tuscaloosa, Tuscaloosa, AL, United States
| | - John Torous
- Department of Psychiatry, Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, MA, United States
| | - Jung Won Kim
- Department of Psychiatry and Behavioral Neurobiology, University of Alabama at Birmingham, Birmingham, AL, United States
| |
Collapse
|