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Liu C, Fan J, Bailey B, Müller RA, Linke A. Assessing Predictive Ability of Dynamic Time Warping Functional Connectivity for ASD Classification. Int J Biomed Imaging 2023; 2023:8512461. [PMID: 37920379 PMCID: PMC10620025 DOI: 10.1155/2023/8512461] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/12/2022] [Revised: 09/11/2023] [Accepted: 09/16/2023] [Indexed: 11/04/2023] Open
Abstract
Functional connectivity MRI (fcMRI) is a technique used to study the functional connectedness of distinct regions of the brain by measuring the temporal correlation between their blood oxygen level-dependent (BOLD) signals. fcMRI is typically measured with the Pearson correlation (PC), which assumes that there is no lag between time series. Dynamic time warping (DTW) is an alternative measure of similarity between time series that is robust to such time lags. We used PC fcMRI data and DTW fcMRI data as predictors in machine learning models for classifying autism spectrum disorder (ASD). When combined with dimension reduction techniques, such as principal component analysis, functional connectivity estimated with DTW showed greater predictive ability than functional connectivity estimated with PC. Our results suggest that DTW fcMRI can be a suitable alternative measure that may be characterizing fcMRI in a different, but complementary, way to PC fcMRI that is worth continued investigation. In studying different variants of cross validation (CV), our results suggest that, when it is necessary to tune model hyperparameters and assess model performance at the same time, a K-fold CV nested within leave-one-out CV may be a competitive contender in terms of performance and computational speed, especially when sample size is not large.
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Affiliation(s)
- Christopher Liu
- Department of Mathematics and Statistics, San Diego State University, California, USA
- Brain Development Imaging Laboratory, Department of Psychology, San Diego State University, California, USA
| | - Juanjuan Fan
- Department of Mathematics and Statistics, San Diego State University, California, USA
| | - Barbara Bailey
- Department of Mathematics and Statistics, San Diego State University, California, USA
| | - Ralph-Axel Müller
- Brain Development Imaging Laboratory, Department of Psychology, San Diego State University, California, USA
| | - Annika Linke
- Brain Development Imaging Laboratory, Department of Psychology, San Diego State University, California, USA
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MAGE: Automatic diagnosis of autism spectrum disorders using multi-atlas graph convolutional networks and ensemble learning. Neurocomputing 2022. [DOI: 10.1016/j.neucom.2020.06.152] [Citation(s) in RCA: 10] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/03/2023]
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Aghdam MA, Sharifi A, Pedram MM. Diagnosis of Autism Spectrum Disorders in Young Children Based on Resting-State Functional Magnetic Resonance Imaging Data Using Convolutional Neural Networks. J Digit Imaging 2021; 32:899-918. [PMID: 30963340 DOI: 10.1007/s10278-019-00196-1] [Citation(s) in RCA: 15] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/15/2022] Open
Abstract
Statistics show that the risk of autism spectrum disorder (ASD) is increasing in the world. Early diagnosis is most important factor in treatment of ASD. Thus far, the childhood diagnosis of ASD has been done based on clinical interviews and behavioral observations. There is a significant need to reduce the use of traditional diagnostic techniques and to diagnose this disorder in the right time and before the manifestation of behavioral symptoms. The purpose of this study is to present the intelligent model to diagnose ASD in young children based on resting-state functional magnetic resonance imaging (rs-fMRI) data using convolutional neural networks (CNNs). CNNs, which are by far one of the most powerful deep learning algorithms, are mainly trained using datasets with large numbers of samples. However, obtaining comprehensive datasets such as ImageNet and achieving acceptable results in medical imaging domain have become challenges. In order to overcome these two challenges, the two methods of "combining classifiers," both dynamic (mixture of experts) and static (simple Bayes) approaches, and "transfer learning" were used in this analysis. In addition, since diagnosis of ASD will be much more effective at an early age, samples ranging in age from 5 to 10 years from global Autism Brain Imaging Data Exchange I and II (ABIDE I and ABIDE II) datasets were used in this research. The accuracy, sensitivity, and specificity of presented model outperform the results of previous studies conducted on ABIDE I dataset (the best results obtained from Adamax optimization technique: accuracy = 0.7273, sensitivity = 0.712, specificity = 0.7348). Furthermore, acceptable classification results were obtained from ABIDE II dataset (the best results obtained from Adamax optimization technique: accuracy = 0.7, sensitivity = 0.582, specificity = 0.804) and the combination of ABIDE I and ABIDE II datasets (the best results obtained from Adam optimization technique: accuracy = 0.7045, sensitivity = 0.679, specificity = 0.7421). We can conclude that the proposed architecture can be considered as an efficient tool for diagnosis of ASD in young children. From another perspective, this proposed method can be applied to analyzing rs-fMRI data related to brain dysfunctions.
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Affiliation(s)
- Maryam Akhavan Aghdam
- Department of Computer Engineering, Science and Research Branch, Islamic Azad University, Tehran, Iran
| | - Arash Sharifi
- Department of Computer Engineering, Science and Research Branch, Islamic Azad University, Tehran, Iran.
| | - Mir Mohsen Pedram
- Department of Electrical and Computer Engineering, Kharazmi University, Tehran, Iran
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Agastinose Ronicko JF, Thomas J, Thangavel P, Koneru V, Langs G, Dauwels J. Diagnostic classification of autism using resting-state fMRI data improves with full correlation functional brain connectivity compared to partial correlation. J Neurosci Methods 2020; 345:108884. [PMID: 32730918 DOI: 10.1016/j.jneumeth.2020.108884] [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: 03/12/2020] [Revised: 07/23/2020] [Accepted: 07/24/2020] [Indexed: 02/06/2023]
Abstract
BACKGROUND Autism Spectrum Disorder (ASD) is a neurodevelopmental disability with altered connectivity in brain networks. NEW METHOD In this study, brain connections in Resting-state functional Magnetic Resonance Imaging (Rs-fMRI) of ASD and Typical Developing (TD) are analyzed by partial and full correlation methods such as Gaussian Graphical Least Absolute Shrinkage and Selection Operator (GLASSO), Max-Det Matrix Completion (MDMC), and Pearson Correlation Co-Efficient (PCCE). We investigated Functional Connectivity (FC) of ASD and TD brain from 238 functionally defined regions of interest. Furthermore, we constructed a series of feature sets by applying conditional random forests and conditional permutation importance. We built classifier models by Random Forest (RF), Oblique RF (ORF), Support Vector Machine (SVM), and Convolutional Neural Network (CNN) for each feature set. FC features are ranked based on p-value and we analyzed the top 20 FC features. RESULTS We achieved a single-trial test accuracy of 72.5 %, though MDMC-SVM and PCCE-CNN pipelines. Further, PCCE-CNN pipeline gives better average test accuracy (70.31 %) and area under the curve (0.73) compared to other pipelines. We found that top-20 PCCE based FC features are from networks such as Dorsal Attention (DA), Cingulo-Opercular Task Control (COTC), somatosensory motor hand and subcortical. In addition, among top 20 PCCE features, many FC links are found between COTC and DA (4 connections) which helped to discriminate the ASD and TD. COMPARISON WITH EXISTING METHODS AND CONCLUSIONS The generalized classifier models built in our study for highly heterogeneous participants perform better than previous studies with similar data sets and diagnostic groups.
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Affiliation(s)
- Jac Fredo Agastinose Ronicko
- School of Electrical and Electronics Engineering, Nanyang Technological University, 50 Nanyang Avenue, 639 798, Singapore.
| | - John Thomas
- School of Electrical and Electronics Engineering, Nanyang Technological University, 50 Nanyang Avenue, 639 798, Singapore.
| | - Prasanth Thangavel
- School of Electrical and Electronics Engineering, Nanyang Technological University, 50 Nanyang Avenue, 639 798, Singapore.
| | - Vineetha Koneru
- School of Electrical and Electronics Engineering, Nanyang Technological University, 50 Nanyang Avenue, 639 798, Singapore.
| | - Georg Langs
- Computational Imaging Research Lab, Department of Biomedical Imaging and Image-guided Therapy, Medical University of Vienna, Waehringer Guertel 18-20, A-1090, Vienna, Austria.
| | - Justin Dauwels
- School of Electrical and Electronics Engineering, Nanyang Technological University, 50 Nanyang Avenue, 639 798, Singapore.
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Wang Y, Wang J, Wu FX, Hayrat R, Liu J. AIMAFE: Autism spectrum disorder identification with multi-atlas deep feature representation and ensemble learning. J Neurosci Methods 2020; 343:108840. [PMID: 32653384 DOI: 10.1016/j.jneumeth.2020.108840] [Citation(s) in RCA: 17] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/12/2019] [Revised: 06/30/2020] [Accepted: 06/30/2020] [Indexed: 01/08/2023]
Abstract
BACKGROUND Autism spectrum disorder (ASD) is a neurodevelopmental disorder that could cause problems in social communications. Clinically, diagnosing ASD mainly relies on behavioral criteria while this approach is not objective enough and could cause delayed diagnosis. Since functional magnetic resonance imaging (fMRI) can measure brain activity, it provides data for the study of brain dysfunction disorders and has been widely used in ASD identification. However, satisfactory accuracy for ASD identification has not been achieved. NEW METHOD To improve the performance of ASD identification, we propose an ASD identification method based on multi-atlas deep feature representation and ensemble learning. We first calculate multiple functional connectivity based on different brain atlases from fMRI data of each subject. Then, to get the more discriminative features for ASD identification, we propose a multi-atlas deep feature representation method based on stacked denoising autoencoder (SDA). Finally, we propose multilayer perceptron (MLP) and an ensemble learning method to perform the final ASD identification task. RESULTS Our proposed method is evaluated on 949 subjects (including 419 ASDs and 530 typical control (TCs)) from the Autism Brain Imaging Data Exchange (ABIDE) and achieves accuracy of 74.52% (sensitivity of 80.69%, specificity of 66.71%, AUC of 0.8026) for ASD identification. COMPARISON WITH EXISTING METHODS Compared with some previously published methods, our proposed method obtains the better performance for ASD identification. CONCLUSION The results suggest that our proposed method is efficient to improve the performance of ASD identification, and is promising for ASD clinical diagnosis.
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Affiliation(s)
- Yufei Wang
- Hunan Provincial Key Lab on Bioinformatics, School of Computer Science and Engineering, Central South University, Changsha 410083, China.
| | - Jianxin Wang
- Hunan Provincial Key Lab on Bioinformatics, School of Computer Science and Engineering, Central South University, Changsha 410083, China.
| | - Fang-Xiang Wu
- Division of Biomedical Engineering and Department of Mechanical Engineering, University of Saskatchewan, Saskatoon S7N 5A9, Canada.
| | - Rahmatjan Hayrat
- Hunan Provincial Key Lab on Bioinformatics, School of Computer Science and Engineering, Central South University, Changsha 410083, China.
| | - Jin Liu
- Hunan Provincial Key Lab on Bioinformatics, School of Computer Science and Engineering, Central South University, Changsha 410083, China.
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Akhavan Aghdam M, Sharifi A, Pedram MM. Combination of rs-fMRI and sMRI Data to Discriminate Autism Spectrum Disorders in Young Children Using Deep Belief Network. J Digit Imaging 2018; 31:895-903. [PMID: 29736781 PMCID: PMC6261184 DOI: 10.1007/s10278-018-0093-8] [Citation(s) in RCA: 56] [Impact Index Per Article: 9.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/07/2023] Open
Abstract
In recent years, the use of advanced magnetic resonance (MR) imaging methods such as functional magnetic resonance imaging (fMRI) and structural magnetic resonance imaging (sMRI) has recorded a great increase in neuropsychiatric disorders. Deep learning is a branch of machine learning that is increasingly being used for applications of medical image analysis such as computer-aided diagnosis. In a bid to classify and represent learning tasks, this study utilized one of the most powerful deep learning algorithms (deep belief network (DBN)) for the combination of data from Autism Brain Imaging Data Exchange I and II (ABIDE I and ABIDE II) datasets. The DBN was employed so as to focus on the combination of resting-state fMRI (rs-fMRI), gray matter (GM), and white matter (WM) data. This was done based on the brain regions that were defined using the automated anatomical labeling (AAL), in order to classify autism spectrum disorders (ASDs) from typical controls (TCs). Since the diagnosis of ASD is much more effective at an early age, only 185 individuals (116 ASD and 69 TC) ranging in age from 5 to 10 years were included in this analysis. In contrast, the proposed method is used to exploit the latent or abstract high-level features inside rs-fMRI and sMRI data while the old methods consider only the simple low-level features extracted from neuroimages. Moreover, combining multiple data types and increasing the depth of DBN can improve classification accuracy. In this study, the best combination comprised rs-fMRI, GM, and WM for DBN of depth 3 with 65.56% accuracy (sensitivity = 84%, specificity = 32.96%, F1 score = 74.76%) obtained via 10-fold cross-validation. This result outperforms previously presented methods on ABIDE I dataset.
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Affiliation(s)
- Maryam Akhavan Aghdam
- Department of Computer Engineering, Science and Research Branch, Islamic Azad University, Tehran, Iran
| | - Arash Sharifi
- Department of Computer Engineering, Science and Research Branch, Islamic Azad University, Tehran, Iran
| | - Mir Mohsen Pedram
- Department of Electrical and Computer Engineering, Kharazmi University, Tehran, Iran
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Bednarz HM, Kana RK. Advances, challenges, and promises in pediatric neuroimaging of neurodevelopmental disorders. Neurosci Biobehav Rev 2018; 90:50-69. [PMID: 29608989 DOI: 10.1016/j.neubiorev.2018.03.025] [Citation(s) in RCA: 12] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/25/2017] [Revised: 02/26/2018] [Accepted: 03/22/2018] [Indexed: 10/17/2022]
Abstract
Recent years have witnessed the proliferation of neuroimaging studies of neurodevelopmental disorders (NDDs), particularly of children with autism spectrum disorder (ASD), attention-deficit/hyperactivity disorder (ADHD), and Tourette's syndrome (TS). Neuroimaging offers immense potential in understanding the biology of these disorders, and how it relates to clinical symptoms. Neuroimaging techniques, in the long run, may help identify neurobiological markers to assist clinical diagnosis and treatment. However, methodological challenges have affected the progress of clinical neuroimaging. This paper reviews the methodological challenges involved in imaging children with NDDs. Specific topics include correcting for head motion, normalization using pediatric brain templates, accounting for psychotropic medication use, delineating complex developmental trajectories, and overcoming smaller sample sizes. The potential of neuroimaging-based biomarkers and the utility of implementing neuroimaging in a clinical setting are also discussed. Data-sharing approaches, technological advances, and an increase in the number of longitudinal, prospective studies are recommended as future directions. Significant advances have been made already, and future decades will continue to see innovative progress in neuroimaging research endeavors of NDDs.
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Affiliation(s)
- Haley M Bednarz
- Department of Psychology, University of Alabama at Birmingham, Birmingham, AL, USA
| | - Rajesh K Kana
- Department of Psychology, University of Alabama at Birmingham, Birmingham, AL, USA.
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Uddin LQ, Dajani DR, Voorhies W, Bednarz H, Kana RK. Progress and roadblocks in the search for brain-based biomarkers of autism and attention-deficit/hyperactivity disorder. Transl Psychiatry 2017; 7:e1218. [PMID: 28892073 PMCID: PMC5611731 DOI: 10.1038/tp.2017.164] [Citation(s) in RCA: 96] [Impact Index Per Article: 13.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/01/2017] [Accepted: 06/07/2017] [Indexed: 11/22/2022] Open
Abstract
Children with neurodevelopmental disorders benefit most from early interventions and treatments. The development and validation of brain-based biomarkers to aid in objective diagnosis can facilitate this important clinical aim. The objective of this review is to provide an overview of current progress in the use of neuroimaging to identify brain-based biomarkers for autism spectrum disorder (ASD) and attention-deficit/hyperactivity disorder (ADHD), two prevalent neurodevelopmental disorders. We summarize empirical work that has laid the foundation for using neuroimaging to objectively quantify brain structure and function in ways that are beginning to be used in biomarker development, noting limitations of the data currently available. The most successful machine learning methods that have been developed and applied to date are discussed. Overall, there is increasing evidence that specific features (for example, functional connectivity, gray matter volume) of brain regions comprising the salience and default mode networks can be used to discriminate ASD from typical development. Brain regions contributing to successful discrimination of ADHD from typical development appear to be more widespread, however there is initial evidence that features derived from frontal and cerebellar regions are most informative for classification. The identification of brain-based biomarkers for ASD and ADHD could potentially assist in objective diagnosis, monitoring of treatment response and prediction of outcomes for children with these neurodevelopmental disorders. At present, however, the field has yet to identify reliable and reproducible biomarkers for these disorders, and must address issues related to clinical heterogeneity, methodological standardization and cross-site validation before further progress can be achieved.
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Affiliation(s)
- L Q Uddin
- Department of Psychology, University of Miami, Coral Gables, FL, USA,Neuroscience Program, University of Miami Miller School of Medicine, Miami, FL, USA,Department of Psychology, University of Miami, P.O. Box 248185-0751, Coral Gables, FL 33124, USA. E-mail:
| | - D R Dajani
- Department of Psychology, University of Miami, Coral Gables, FL, USA
| | - W Voorhies
- Department of Psychology, University of Miami, Coral Gables, FL, USA
| | - H Bednarz
- Department of Psychology, University of Alabama at Birmingham, Birmingham, AL, USA
| | - R K Kana
- Department of Psychology, University of Alabama at Birmingham, Birmingham, AL, USA
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Liu W, Li M, Yi L. Identifying children with autism spectrum disorder based on their face processing abnormality: A machine learning framework. Autism Res 2016; 9:888-98. [PMID: 27037971 DOI: 10.1002/aur.1615] [Citation(s) in RCA: 82] [Impact Index Per Article: 10.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/02/2015] [Revised: 01/29/2016] [Accepted: 01/30/2016] [Indexed: 01/23/2023]
Abstract
The atypical face scanning patterns in individuals with Autism Spectrum Disorder (ASD) has been repeatedly discovered by previous research. The present study examined whether their face scanning patterns could be potentially useful to identify children with ASD by adopting the machine learning algorithm for the classification purpose. Particularly, we applied the machine learning method to analyze an eye movement dataset from a face recognition task [Yi et al., 2016], to classify children with and without ASD. We evaluated the performance of our model in terms of its accuracy, sensitivity, and specificity of classifying ASD. Results indicated promising evidence for applying the machine learning algorithm based on the face scanning patterns to identify children with ASD, with a maximum classification accuracy of 88.51%. Nevertheless, our study is still preliminary with some constraints that may apply in the clinical practice. Future research should shed light on further valuation of our method and contribute to the development of a multitask and multimodel approach to aid the process of early detection and diagnosis of ASD. Autism Res 2016, 9: 888-898. © 2016 International Society for Autism Research, Wiley Periodicals, Inc.
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Affiliation(s)
- Wenbo Liu
- From the Sun Yat-sen University Carnegie Mellon University Joint Institute of Engineering, Sun Yat-sen University, Guangzhou Higher Education Mega Center, Guangzhou, China.,Department of ECE, Carnegie Mellon University, Pittsburgh, PA, USA
| | - Ming Li
- Sun Yat-sen University Carnegie Mellon University Shunde International Joint Research Institute, Shunde, Guangdong, China
| | - Li Yi
- Department of Psychology and Beijing Key Laboratory of Behavior and Mental Health, Peking University, Beijing, China
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Use of Machine Learning to Identify Children with Autism and Their Motor Abnormalities. J Autism Dev Disord 2016; 45:2146-56. [PMID: 25652603 DOI: 10.1007/s10803-015-2379-8] [Citation(s) in RCA: 104] [Impact Index Per Article: 13.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/27/2022]
Abstract
In the present work, we have undertaken a proof-of-concept study to determine whether a simple upper-limb movement could be useful to accurately classify low-functioning children with autism spectrum disorder (ASD) aged 2-4. To answer this question, we developed a supervised machine-learning method to correctly discriminate 15 preschool children with ASD from 15 typically developing children by means of kinematic analysis of a simple reach-to-drop task. Our method reached a maximum classification accuracy of 96.7% with seven features related to the goal-oriented part of the movement. These preliminary findings offer insight into a possible motor signature of ASD that may be potentially useful in identifying a well-defined subset of patients, reducing the clinical heterogeneity within the broad behavioral phenotype.
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Abstract
Network perspectives, in their emphasis on components and their interactions, might afford the best approach to the complexities of the ASD realm. Categorical approaches are unlikely to be fruitful as one should not expect to find a single or even predominant underlying cause of autism behavior across individuals. It is possible that the complex, highly interactive, heterogeneous and individualistic nature of the autism realm is intractable in terms of identifying clinically useful biomarker tests. It is hopeful from an emergenic perspective that small corrective changes in a single component of a deleterious network/configuration might have large beneficial consequences on developmental trajectories and in later treatment. It is suggested that the relationship between ASD and intellectual disability might be fundamentally different in single-gene versus nonsyndromic ASD. It is strongly stated that available biomarker "tests" for autism/ASD will do more harm than good. Finally, the serotonin-melatonin-oxidative stress-placental intersection might be an especially fruitful area of biological investigation.
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Functional connectivity classification of autism identifies highly predictive brain features but falls short of biomarker standards. NEUROIMAGE-CLINICAL 2014; 7:359-66. [PMID: 25685703 PMCID: PMC4309950 DOI: 10.1016/j.nicl.2014.12.013] [Citation(s) in RCA: 163] [Impact Index Per Article: 16.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 10/29/2014] [Revised: 12/12/2014] [Accepted: 12/22/2014] [Indexed: 12/04/2022]
Abstract
Objectives Autism spectrum disorders (ASD) are diagnosed based on early-manifesting clinical symptoms, including markedly impaired social communication. We assessed the viability of resting-state functional MRI (rs-fMRI) connectivity measures as diagnostic biomarkers for ASD and investigated which connectivity features are predictive of a diagnosis. Methods Rs-fMRI scans from 59 high functioning males with ASD and 59 age- and IQ-matched typically developing (TD) males were used to build a series of machine learning classifiers. Classification features were obtained using 3 sets of brain regions. Another set of classifiers was built from participants' scores on behavioral metrics. An additional age and IQ-matched cohort of 178 individuals (89 ASD; 89 TD) from the Autism Brain Imaging Data Exchange (ABIDE) open-access dataset (http://fcon_1000.projects.nitrc.org/indi/abide/) were included for replication. Results High classification accuracy was achieved through several rs-fMRI methods (peak accuracy 76.67%). However, classification via behavioral measures consistently surpassed rs-fMRI classifiers (peak accuracy 95.19%). The class probability estimates, P(ASD|fMRI data), from brain-based classifiers significantly correlated with scores on a measure of social functioning, the Social Responsiveness Scale (SRS), as did the most informative features from 2 of the 3 sets of brain-based features. The most informative connections predominantly originated from regions strongly associated with social functioning. Conclusions While individuals can be classified as having ASD with statistically significant accuracy from their rs-fMRI scans alone, this method falls short of biomarker standards. Classification methods provided further evidence that ASD functional connectivity is characterized by dysfunction of large-scale functional networks, particularly those involved in social information processing. We distinguish rs-fMRI scans from ASD and TD individuals with high accuracy. ASD versus TD classification using behavioral metrics was much more accurate. Highly predictive brain features largely originated from the canonical social brain. High performing brain features also correlated with individual symptom severity.
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Neurobiological abnormalities in the first few years of life in individuals later diagnosed with autism spectrum disorder: a review of recent data. Behav Neurol 2014; 2014:210780. [PMID: 24825948 PMCID: PMC4006615 DOI: 10.1155/2014/210780] [Citation(s) in RCA: 17] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/22/2013] [Accepted: 06/23/2013] [Indexed: 02/07/2023] Open
Abstract
Background. Despite the widely-held understanding that the biological changes that lead to autism usually occur during prenatal life, there has been relatively little research into the functional development of the brain during early infancy in individuals later diagnosed with autism spectrum disorder (ASD). Objective. This review explores the studies over the last three years which have investigated differences in various brain regions in individuals with ASD or who later go on to receive a diagnosis of ASD. Methods. We used PRISMA guidelines and selected published articles reporting any neurological abnormalities in very early childhood in individuals with or later diagnosed with ASD. Results. Various brain regions are discussed including the amygdala, cerebellum, frontal cortex, and lateralised abnormalities of the temporal cortex during language processing. This review discusses studies investigating head circumference, electrophysiological markers, and interhemispheric synchronisation. All of the recent findings from the beginning of 2009 across these different aspects of defining neurological abnormalities are discussed in light of earlier findings. Conclusions. The studies across these different areas reveal the existence of atypicalities in the first year of life, well before ASD is reliably diagnosed. Cross-disciplinary approaches are essential to elucidate the pathophysiological sequence of events that lead to ASD.
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Taylor LJ, Maybery MT, Whitehouse AJO. Moving beyond behaviour-only assessment: incorporating biomarkers to improve the early detection and diagnosis of autism spectrum disorders. INTERNATIONAL JOURNAL OF SPEECH-LANGUAGE PATHOLOGY 2014; 16:19-22. [PMID: 24236911 DOI: 10.3109/17549507.2013.855262] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/02/2023]
Abstract
This paper presents a response to the Camarata (2014) lead article regarding the accuracy and effectiveness of early identification and early intervention for young children with autism spectrum disorders (ASD). While Caramata focused heavily on the challenges of behavioural screening for ASD, we believe that he has overlooked the potential that the identification of ASD biomarkers may have for the early detection of the disorder. We propose that the discovery of biomarkers, particularly those that may be used in conjunction with behavioural screening, may provide an important next step in reliably detecting and accurately diagnosing ASD in the early years. This would have important clinical implications in terms of providing early intervention, which may alter the developmental path for the child.
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Affiliation(s)
- Lauren J Taylor
- Neurocognitive Development Unit, School of Psychology, University of Western Australia , Perth , Australia
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