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Zhang H, Chen J, Liao B, Wu FX, Bi XA. Deep Canonical Correlation Fusion Algorithm Based on Denoising Autoencoder for ASD Diagnosis and Pathogenic Brain Region Identification. Interdiscip Sci 2024; 16:455-468. [PMID: 38573456 DOI: 10.1007/s12539-024-00625-y] [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: 12/07/2023] [Revised: 02/22/2024] [Accepted: 02/25/2024] [Indexed: 04/05/2024]
Abstract
Autism Spectrum Disorder (ASD) is defined as a neurodevelopmental condition distinguished by unconventional neural activities. Early intervention is key to managing the progress of ASD, and current research primarily focuses on the use of structural magnetic resonance imaging (sMRI) or resting-state functional magnetic resonance imaging (rs-fMRI) for diagnosis. Moreover, the use of autoencoders for disease classification has not been sufficiently explored. In this study, we introduce a new framework based on autoencoder, the Deep Canonical Correlation Fusion algorithm based on Denoising Autoencoder (DCCF-DAE), which proves to be effective in handling high-dimensional data. This framework involves efficient feature extraction from different types of data with an advanced autoencoder, followed by the fusion of these features through the DCCF model. Then we utilize the fused features for disease classification. DCCF integrates functional and structural data to help accurately diagnose ASD and identify critical Regions of Interest (ROIs) in disease mechanisms. We compare the proposed framework with other methods by the Autism Brain Imaging Data Exchange (ABIDE) database and the results demonstrate its outstanding performance in ASD diagnosis. The superiority of DCCF-DAE highlights its potential as a crucial tool for early ASD diagnosis and monitoring.
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Affiliation(s)
- Huilian Zhang
- Key Laboratory of Data Science and Intelligence Education, Ministry of Education, Hainan Normal University, Haikou, 571126, China
- College of Mathematics and Statistics, Hainan Normal University, Haikou, 571126, China
| | - Jie Chen
- Key Laboratory of Data Science and Intelligence Education, Ministry of Education, Hainan Normal University, Haikou, 571126, China
- College of Mathematics and Statistics, Hainan Normal University, Haikou, 571126, China
| | - Bo Liao
- Key Laboratory of Data Science and Intelligence Education, Ministry of Education, Hainan Normal University, Haikou, 571126, China
- College of Mathematics and Statistics, Hainan Normal University, Haikou, 571126, China
| | - Fang-Xiang Wu
- Division of Biomedical Engineering, University of Saskatchewan, Saskatoon, S7N5A9, Canada
| | - Xia-An Bi
- Key Laboratory of Data Science and Intelligence Education, Ministry of Education, Hainan Normal University, Haikou, 571126, China.
- College of Mathematics and Statistics, Hainan Normal University, Haikou, 571126, China.
- College of Information Science and Engineering, Hunan Normal University, Changsha, Hunan, 410081, China.
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Mukherjee D, Bhavnani S, Lockwood Estrin G, Rao V, Dasgupta J, Irfan H, Chakrabarti B, Patel V, Belmonte MK. Digital tools for direct assessment of autism risk during early childhood: A systematic review. AUTISM : THE INTERNATIONAL JOURNAL OF RESEARCH AND PRACTICE 2024; 28:6-31. [PMID: 36336996 PMCID: PMC10771029 DOI: 10.1177/13623613221133176] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/09/2022]
Abstract
LAY ABSTRACT The challenge of finding autistic children, and finding them early enough to make a difference for them and their families, becomes all the greater in parts of the world where human and material resources are in short supply. Poverty of resources delays interventions, translating into a poverty of outcomes. Digital tools carry potential to lessen this delay because they can be administered by non-specialists in children's homes, schools or other everyday environments, they can measure a wide range of autistic behaviours objectively and they can automate analysis without requiring an expert in computers or statistics. This literature review aimed to identify and describe digital tools for screening children who may be at risk for autism. These tools are predominantly at the 'proof-of-concept' stage. Both portable (laptops, mobile phones, smart toys) and fixed (desktop computers, virtual-reality platforms) technologies are used to present computerised games, or to record children's behaviours or speech. Computerised analysis of children's interactions with these technologies differentiates children with and without autism, with promising results. Tasks assessing social responses and hand and body movements are the most reliable in distinguishing autistic from typically developing children. Such digital tools hold immense potential for early identification of autism spectrum disorder risk at a large scale. Next steps should be to further validate these tools and to evaluate their applicability in a variety of settings. Crucially, stakeholders from underserved communities globally must be involved in this research, lest it fail to capture the issues that these stakeholders are facing.
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Affiliation(s)
- Debarati Mukherjee
- Indian Institute of Public Health - Bengaluru, Public Health Foundation of India, India
| | | | | | - Vaisnavi Rao
- Institute for Democracy and Economic Affairs (IDEAS), Malaysia
| | | | | | | | - Vikram Patel
- Child Development Group, Sangath, India
- Harvard Medical School, USA
- Harvard T.H. Chan School of Public Health, USA
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Faraji R, Ganji Z, Zamanpour SA, Nikparast F, Akbari-Lalimi H, Zare H. Impaired white matter integrity in infants and young children with autism spectrum disorder: What evidence does diffusion tensor imaging provide? Psychiatry Res Neuroimaging 2023; 335:111711. [PMID: 37741094 DOI: 10.1016/j.pscychresns.2023.111711] [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: 12/26/2022] [Revised: 02/26/2023] [Accepted: 08/26/2023] [Indexed: 09/25/2023]
Abstract
BACKGROUND Abnormal functional connections are associated with impaired white matter tract integrity in the brain. Diffusion tensor imaging (DTI) is a promising method for evaluating white matter integrity in infants and young children. This work aims to shed light on the location and nature of the decrease in white matter integrity. METHODS Here, the results of 19 studies have been presented that investigated white matter integrity in infants and young children (6 months to 12 years) with autism using diffusion tensor imaging. RESULTS In most of the reviewed studies, an increase in Fractional Anisotropy (FA) and a decrease in Radial Diffusivity (RD) were reported in Corpus Callosum (CC), Uncinate Fasciculus (UF), Cingulum (Cg), Inferior Longitudinal Fasciculus (ILF), and Superior Longitudinal Fasciculus (SLF), and in the Inferior Fronto-Occipital Fasciculus (IFOF) tract, a decrease in FA and an increase in RD were reported. CONCLUSION In the reviewed articles, except for one study, the diffusion indices were different compared to the control group.
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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
| | - Seyed Amir Zamanpour
- Medical Physics Research Center, Mashhad University of Medical Sciences, Mashhad, Iran
| | - Farzaneh Nikparast
- 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
| | - 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.
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Yang J, Wang F, Li Z, Yang Z, Dong X, Han Q. Constructing high-order functional networks based on hypergraph for diagnosis of autism spectrum disorders. Front Neurosci 2023; 17:1257982. [PMID: 37719159 PMCID: PMC10501447 DOI: 10.3389/fnins.2023.1257982] [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: 07/13/2023] [Accepted: 08/17/2023] [Indexed: 09/19/2023] Open
Abstract
Introduction High-order functional connectivity networks (FCNs) that reflect the connection relationships among multiple brain regions have become important tools for exploring the deep workings of the brain and revealing the mechanisms of brain diseases. The traditional high-order FCN constructed based on the "correlation of correlations" strategy, is a representative method for conducting whole-brain connectivity analysis and revealing global network characteristics. However, whole-brain connectivity analysis may be affected by noise carried by less important brain regions, resulting in redundant information and affecting the accuracy and reliability of the analysis. Moreover, this type of analysis has a high computational complexity. Methods To address these issues, a new method for constructing high-order FCN based on hypergraphs is proposed in this article, which is used to accurately capture the real interaction relationships among brain regions. Specifically, first, a low-order FCN reflecting the connection relationships between pairs of brain regions based on resting-state functional Magnetic Resonance Imaging (rs-fMRI) time series is constructed, the method first constructs the low-order FCN that reflects the connection relationships between pairs of brain regions based on rs-fMRI time series, and then selects the "good friends" of each brain region from hypergraph perspective, which refers to the local friend circles with closer relationships. Then, the rs-fMRI time series corresponding to the "good friends" in each brain region's friend circle are averaged to obtain a sequence that reflects the intimacy between brain regions in each friend circle. Finally, hypergraph high-order FCN, which reflects the interaction relationships among multiple brain regions, is obtained by calculating the correlations based on the sequence of friend circles. Results The experimental results demonstrate that the proposed method outperforms traditional high-order FCN construction methods. Furthermore, integrating the high-order FCN constructed based on hypergraphs and the low-order FCN through feature fusion to achieve complementary information improves the accuracy of assisting in the diagnosis of brain diseases. Discussion In addition, the effectiveness of our method has only been validated in the diagnosis of ASD. For future work, we plan to extend this method to other brain connectivity patterns.
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Affiliation(s)
- Jie Yang
- Faculty of Nature, Mathematical & Engineering Sciences, King’s College London, London, United Kingdom
| | - Fang Wang
- School of Information Science and Engineering, Zaozhuang University, Zaozhuang, China
| | - Zhen Li
- Hydrological Center of Zaozhuang, Zaozhuang, China
| | - Zhen Yang
- School of Artificial Intelligence, Zaozhuang University, Zaozhuang, China
| | - Xishang Dong
- School of Information Science and Engineering, Zaozhuang University, Zaozhuang, China
| | - Qinghua Han
- School of Artificial Intelligence, Zaozhuang University, Zaozhuang, China
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Helmy E, Elnakib A, ElNakieb Y, Khudri M, Abdelrahim M, Yousaf J, Ghazal M, Contractor S, Barnes GN, El-Baz A. Role of Artificial Intelligence for Autism Diagnosis Using DTI and fMRI: A Survey. Biomedicines 2023; 11:1858. [PMID: 37509498 PMCID: PMC10376963 DOI: 10.3390/biomedicines11071858] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/26/2023] [Revised: 06/22/2023] [Accepted: 06/27/2023] [Indexed: 07/30/2023] Open
Abstract
Autism spectrum disorder (ASD) is a wide range of diseases characterized by difficulties with social skills, repetitive activities, speech, and nonverbal communication. The Centers for Disease Control (CDC) estimates that 1 in 44 American children currently suffer from ASD. The current gold standard for ASD diagnosis is based on behavior observational tests by clinicians, which suffer from being subjective and time-consuming and afford only late detection (a child must have a mental age of at least two to apply for an observation report). Alternatively, brain imaging-more specifically, magnetic resonance imaging (MRI)-has proven its ability to assist in fast, objective, and early ASD diagnosis and detection. With the recent advances in artificial intelligence (AI) and machine learning (ML) techniques, sufficient tools have been developed for both automated ASD diagnosis and early detection. More recently, the development of deep learning (DL), a young subfield of AI based on artificial neural networks (ANNs), has successfully enabled the processing of brain MRI data with improved ASD diagnostic abilities. This survey focuses on the role of AI in autism diagnostics and detection based on two basic MRI modalities: diffusion tensor imaging (DTI) and functional MRI (fMRI). In addition, the survey outlines the basic findings of DTI and fMRI in autism. Furthermore, recent techniques for ASD detection using DTI and fMRI are summarized and discussed. Finally, emerging tendencies are described. The results of this study show how useful AI is for early, subjective ASD detection and diagnosis. More AI solutions that have the potential to be used in healthcare settings will be introduced in the future.
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Affiliation(s)
- Eman Helmy
- Department of Diagnostic Radiology, Faculty of Medicine, Mansoura University, Elgomheryia Street, Mansoura 3512, Egypt;
| | - Ahmed Elnakib
- Bioengineering Department, University of Louisville, Louisville, KY 40292, USA; (A.E.); (Y.E.); (M.K.); (M.A.)
| | - Yaser ElNakieb
- Bioengineering Department, University of Louisville, Louisville, KY 40292, USA; (A.E.); (Y.E.); (M.K.); (M.A.)
| | - Mohamed Khudri
- Bioengineering Department, University of Louisville, Louisville, KY 40292, USA; (A.E.); (Y.E.); (M.K.); (M.A.)
| | - Mostafa Abdelrahim
- Bioengineering Department, University of Louisville, Louisville, KY 40292, USA; (A.E.); (Y.E.); (M.K.); (M.A.)
| | - Jawad Yousaf
- Electrical, Computer, and Biomedical Engineering Department, Abu Dhabi University, Abu Dhabi 59911, United Arab Emirates; (J.Y.); (M.G.)
| | - Mohammed Ghazal
- Electrical, Computer, and Biomedical Engineering Department, Abu Dhabi University, Abu Dhabi 59911, United Arab Emirates; (J.Y.); (M.G.)
| | - Sohail Contractor
- Department of Radiology, University of Louisville, Louisville, KY 40202, USA;
| | - Gregory Neal Barnes
- Department of Neurology, Pediatric Research Institute, University of Louisville, Louisville, KY 40202, USA;
| | - Ayman El-Baz
- Bioengineering Department, University of Louisville, Louisville, KY 40292, USA; (A.E.); (Y.E.); (M.K.); (M.A.)
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Geng X, Fan X, Zhong Y, Casanova MF, Sokhadze EM, Li X, Kang J. Abnormalities of EEG Functional Connectivity and Effective Connectivity in Children with Autism Spectrum Disorder. Brain Sci 2023; 13:brainsci13010130. [PMID: 36672111 PMCID: PMC9857308 DOI: 10.3390/brainsci13010130] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/16/2022] [Revised: 01/05/2023] [Accepted: 01/10/2023] [Indexed: 01/13/2023] Open
Abstract
Autism spectrum disorder (ASD) is a heterogeneous neurodevelopmental disorder that interferes with normal brain development. Brain connectivity may serve as a biomarker for ASD in this respect. This study enrolled a total of 179 children aged 3-10 years (90 typically developed (TD) and 89 with ASD). We used a weighted phase lag index and a directed transfer function to investigate the functional and effective connectivity in children with ASD and TD. Our findings indicated that patients with ASD had local hyper-connectivity of brain regions in functional connectivity and simultaneous significant decrease in effective connectivity across hemispheres. These connectivity abnormalities may help to find biomarkers of ASD.
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Affiliation(s)
- Xinling Geng
- School of Biomedical Engineering, Capital Medical University, Beijing 100069, China
| | - Xiwang Fan
- Clinical Research Center for Mental Disorders, Shanghai Pudong New Area Mental Health Center, School of Medicine, Tongji University, Shanghai 200124, China
| | - Yiwen Zhong
- Clinical Research Center for Mental Disorders, Shanghai Pudong New Area Mental Health Center, School of Medicine, Tongji University, Shanghai 200124, China
| | - Manuel F. Casanova
- Department of Biomedical Sciences, University of South Carolina School of Medicine Greenville, 701 Grove Rd, Greenville, SC 29605, USA
| | - Estate M. Sokhadze
- Department of Biomedical Sciences, University of South Carolina School of Medicine Greenville, 701 Grove Rd, Greenville, SC 29605, USA
| | - Xiaoli Li
- State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing 100859, China
- Correspondence: (X.L.); (J.K.)
| | - Jiannan Kang
- College of Electronic & Information Engineering, Hebei University, Baoding 071000, China
- Correspondence: (X.L.); (J.K.)
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Identifying neuroanatomical and behavioral features for autism spectrum disorder diagnosis in children using machine learning. PLoS One 2022; 17:e0269773. [PMID: 35797364 PMCID: PMC9262216 DOI: 10.1371/journal.pone.0269773] [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: 03/24/2021] [Accepted: 05/27/2022] [Indexed: 11/19/2022] Open
Abstract
Autism spectrum disorder (ASD) is a neurodevelopmental disorder that can cause significant social, communication, and behavioral challenges. Diagnosis of ASD is complicated and there is an urgent need to identify ASD-associated biomarkers and features to help automate diagnostics and develop predictive ASD models. The present study adopts a novel evolutionary algorithm, the conjunctive clause evolutionary algorithm (CCEA), to select features most significant for distinguishing individuals with and without ASD, and is able to accommodate datasets having a small number of samples with a large number of feature measurements. The dataset is unique and comprises both behavioral and neuroimaging measurements from a total of 28 children from 7 to 14 years old. Potential biomarker candidates identified include brain volume, area, cortical thickness, and mean curvature in specific regions around the cingulate cortex, frontal cortex, and temporal-parietal junction, as well as behavioral features associated with theory of mind. A separate machine learning classifier (i.e., k-nearest neighbors algorithm) was used to validate the CCEA feature selection and for ASD prediction. Study findings demonstrate how machine learning tools might help move the needle on improving diagnostic and predictive models of ASD.
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A study of brain networks for autism spectrum disorder classification using resting-state functional connectivity. MACHINE LEARNING WITH APPLICATIONS 2022. [DOI: 10.1016/j.mlwa.2022.100290] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/28/2022] Open
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9
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Efficient Diagnosis of Autism with Optimized Machine Learning Models: An Experimental Analysis on Genetic and Personal Characteristic Datasets. APPLIED SCIENCES-BASEL 2022. [DOI: 10.3390/app12083812] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/23/2022]
Abstract
Early diagnosis of autism is extremely beneficial for patients. Traditional diagnosis approaches have been unable to diagnose autism in a fast and accurate way; rather, there are multiple factors that can be related to identifying the autism disorder. The gene expression (GE) of individuals may be one of these factors, in addition to personal and behavioral characteristics (PBC). Machine learning (ML) based on PBC and GE data analytics emphasizes the need to develop accurate prediction models. The quality of prediction relies on the accuracy of the ML model. To improve the accuracy of prediction, optimized feature selection algorithms are applied to solve the high dimensionality problem of the datasets used. Comparing different optimized feature selection methods using bio-inspired algorithms over different types of data can allow for the most accurate model to be identified. Therefore, in this paper, we investigated enhancing the classification process of autism spectrum disorder using 16 proposed optimized ML models (GWO-NB, GWO-SVM, GWO-KNN, GWO-DT, FPA-NB, FPA-KNN, FPA-SVM, FPA-DT, BA-NB, BA-SVM, BA-KNN, BA-DT, ABC-NB, ABC-SVM, ABV-KNN, and ABC-DT). Four bio-inspired algorithms namely, Gray Wolf Optimization (GWO), Flower Pollination Algorithm (FPA), Bat Algorithms (BA), and Artificial Bee Colony (ABC), were employed for optimizing the wrapper feature selection method in order to select the most informative features and to increase the accuracy of the classification models. Five evaluation metrics were used to evaluate the performance of the proposed models: accuracy, F1 score, precision, recall, and area under the curve (AUC). The obtained results demonstrated that the proposed models achieved a good performance as expected, with accuracies of 99.66% and 99.34% obtained by the GWO-SVM model on the PBC and GE datasets, respectively.
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Dwyer D, Koutsouleris N. Annual Research Review: Translational machine learning for child and adolescent psychiatry. J Child Psychol Psychiatry 2022; 63:421-443. [PMID: 35040130 DOI: 10.1111/jcpp.13545] [Citation(s) in RCA: 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.
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Affiliation(s)
- Dominic Dwyer
- Department of Psychiatry and Psychotherapy, Ludwig-Maximilian-University, Munich, Germany.,Orygen, The National Centre of Excellence in Youth Mental Health, Melbourne, Australia.,Centre for Youth Mental Health, University of Melbourne, Melbourne, Australia
| | - Nikolaos Koutsouleris
- Department of Psychiatry and Psychotherapy, Ludwig-Maximilian-University, Munich, Germany.,Max-Planck Institute of Psychiatry, Munich, Germany.,Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, United Kingdom
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Clairmont C, Wang J, Tariq S, Sherman HT, Zhao M, Kong XJ. The Value of Brain Imaging and Electrophysiological Testing for Early Screening of Autism Spectrum Disorder: A Systematic Review. Front Neurosci 2022; 15:812946. [PMID: 35185452 PMCID: PMC8851356 DOI: 10.3389/fnins.2021.812946] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/10/2021] [Accepted: 12/09/2021] [Indexed: 11/13/2022] Open
Abstract
Given the significance of validating reliable tests for the early detection of autism spectrum disorder (ASD), this systematic review aims to summarize available evidence of neuroimaging and neurophysiological changes in high-risk infants to improve ASD early diagnosis. We included peer-reviewed, primary research in English published before May 21, 2021, involving the use of magnetic resonance imaging (MRI), electroencephalogram (EEG), or functional near-infrared spectroscopy (fNIRS) in children with high risk for ASD under 24 months of age. The main exclusion criteria includes diagnosis of a genetic disorder and gestation age of less the 36 weeks. Online research was performed on PubMed, Web of Science, PsycINFO, and CINAHL. Article selection was conducted by two reviewers to minimize bias. This research was funded by Massachusetts General Hospital Sundry funding. IRB approval was not submitted as it was deemed unnecessary. We included 75 primary research articles. Studies showed that high-risk infants had divergent developmental trajectories for fractional anisotropy and regional brain volumes, increased CSF volume, and global connectivity abnormalities on MRI, decreased sensitivity for familiar faces, atypical lateralization during facial and auditory processing, and different spectral powers across multiple band frequencies on EEG, and distinct developmental trajectories in functional connectivity and regional oxyhemoglobin concentrations in fNIRS. These findings in infants were found to be correlated with the core ASD symptoms and diagnosis at toddler age. Despite the lack of quantitative analysis of the research database, neuroimaging and electrophysiological biomarkers have promising value for the screening of ASD as early as infancy with high accuracy, which warrants further investigation.
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Affiliation(s)
- Cullen Clairmont
- Synapse Lab, Athinoula A. Martinos Center, Massachusetts General Hospital, Boston, MA, United States
| | - Jiuju Wang
- Synapse Lab, Athinoula A. Martinos Center, Massachusetts General Hospital, Boston, MA, United States
- NHC Key Laboratory of Mental Health, National Clinical Research Center for Mental Disorders, Peking University Sixth Hospital, Beijing, China
| | - Samia Tariq
- Synapse Lab, Athinoula A. Martinos Center, Massachusetts General Hospital, Boston, MA, United States
| | - Hannah Tayla Sherman
- Synapse Lab, Athinoula A. Martinos Center, Massachusetts General Hospital, Boston, MA, United States
| | - Mingxuan Zhao
- Department of Business Analytics, Bentley University, Waltham, MA, United States
| | - Xue-Jun Kong
- Synapse Lab, Athinoula A. Martinos Center, Massachusetts General Hospital, Boston, MA, United States
- Department of Psychiatry, Beth Israel Deaconess Medical Center, Boston, MA, United States
- *Correspondence: Xue-Jun Kong,
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Siddiqui S, Gunaseelan L, Shaikh R, Khan A, Mankad D, Hamid MA. Food for Thought: Machine Learning in Autism Spectrum Disorder Screening of Infants. Cureus 2021; 13:e18721. [PMID: 34790476 PMCID: PMC8584605 DOI: 10.7759/cureus.18721] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 10/08/2021] [Indexed: 11/05/2022] Open
Abstract
Diagnoses of autism spectrum disorders (ASD) are typically made after toddlerhood by examining behavioural patterns. Earlier identification of ASD enables earlier intervention and better outcomes. Machine learning provides a data-driven approach of diagnosing autism at an earlier age. This review aims to summarize recent studies and technologies utilizing machine learning based strategies to screen infants and children under the age of 18 months for ASD, and identify gaps that can be addressed in the future. We reviewed nine studies based on our search criteria, which includes primary studies and technologies conducted within the last 10 years that examine children with ASD or at high risk of ASD with a mean age of less than 18 months old. The studies must use machine learning analysis of behavioural features of ASD as major methodology. A total of nine studies were reviewed, of which the sensitivity ranges from 60.7% to 95.6%, the specificity ranges from 50% to 100%, and the accuracy ranges from 60.9% to 97.7%. Factors that contribute to the inconsistent findings include the varied presentation of ASD among patients and study design differences. Previous studies have shown moderate accuracy, sensitivity and specificity in the differentiation of ASD and non-ASD individuals under the age of 18 months. The application of machine learning and artificial intelligence in the screening of ASD in infants is still in its infancy, as observed by the granularity of data available for review. As such, much work needs to be done before the aforementioned technologies can be applied into clinical practice to facilitate early screening of ASD.
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Affiliation(s)
- Sohaib Siddiqui
- Department of Obstetrics and Gynaecology, Women's College Hospital, Toronto, CAN
| | - Luxhman Gunaseelan
- Department of Pediatrics, Saba University School of Medicine, The Bottom, BES
| | - Roohab Shaikh
- Department of Family Medicine, University of British Columbia, Vancouver, CAN
| | - Ahmed Khan
- Department of Pediatrics, Southern Health & Social Care NHS Trust, London, GBR
| | - Deepali Mankad
- Department of Developmental Pediatrics, University of Toronto, Toronto, CAN
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Chen Z, Lei H, Huang Z, Lei B. Latent Space Learning and Feature Learning using Multi-template for Multi-classification of Alzheimer's Disease. ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. ANNUAL INTERNATIONAL CONFERENCE 2021; 2021:1844-1847. [PMID: 34891646 DOI: 10.1109/embc46164.2021.9630795] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/14/2023]
Abstract
Alzheimer's disease (AD) is a common brain disease in the elderly that leads to thinking, memory, and behavior disorders. As the population ages, the proportion of AD patients is also increasing. Accordingly, computer-aided diagnosis of AD attracts more and more attention recently. In this paper, we propose a novel model combining latent space learning and feature learning using features extracted from multiple templates for AD multi-classification. Specifically, latent space learning is employed to obtain the inter-relationship between multiple templates, and feature learning is performed to explore the intrinsic relation in feature space. Finally, the most discriminative features are selected to boost the multi-classification performance. Our proposed model uses the data from the Alzheimer's disease neuroimaging initiative dataset. Furthermore, a series of comparative experiments indicate that our proposed model is quite competitive.
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14
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Zhao F, Zhang X, Thung KH, Mao N, Lee SW, Shen D. Constructing Multi-view High-order Functional Connectivity Networks for Diagnosis of Autism Spectrum Disorder. IEEE Trans Biomed Eng 2021; 69:1237-1250. [PMID: 34705632 DOI: 10.1109/tbme.2021.3122813] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/05/2022]
Abstract
Brain functional connectivity network (FCN) based on resting-state functional magnetic resonance imaging (rs-fMRI) has been widely used to identify neuropsychiatric disorders such as autism spectrum disorder (ASD). Most existing FCN-based methods only estimate the correlation between brain regions of in terest (ROIs), without exploring more informative higher-level inter actions among multiple ROIs which could be beneficial to disease diagnosis. To fully explore the discriminative information provided by different brain networks, a cluster-based multi-view high-order FCN (Ho-FCN) framework is proposed in this paper. Specifically, we first group the functional connectivity (FC) time series into different clusters and compute the multi-order central moment series for the FC time series in each cluster. Then we utilize the correlation of central moment series between different clusters to reveal the high-order FC relationships among multiple ROIs. In addition, to address the phase mismatch issue in conventional FCNs, we also adopt the central moments of the correlation time series as the temporal-invariance features to capture the dynamic characteristics of low-order dynamic FCN (Lo-D-FCN). Experimental results on the ABIDE dataset validate that: 1) the proposed multi-view Ho-FCNs is able to explore rich discriminative information for ASD diagnosis; 2) the phase mismatch issue can be well circumvented by using central moments; and 3) the combination of different types of FCNs can significantly improve the diagnostic accuracy of ASD (86.2%).
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15
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Ahammed MS, Niu S, Ahmed MR, Dong J, Gao X, Chen Y. DarkASDNet: Classification of ASD on Functional MRI Using Deep Neural Network. Front Neuroinform 2021; 15:635657. [PMID: 34248531 PMCID: PMC8265393 DOI: 10.3389/fninf.2021.635657] [Citation(s) in RCA: 14] [Impact Index Per Article: 4.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/30/2020] [Accepted: 04/26/2021] [Indexed: 12/17/2022] Open
Abstract
Non-invasive whole-brain scans aid the diagnosis of neuropsychiatric disorder diseases such as autism, dementia, and brain cancer. The assessable analysis for autism spectrum disorders (ASD) is rationally challenging due to the limitations of publicly available datasets. For diagnostic or prognostic tools, functional Magnetic Resonance Imaging (fMRI) exposed affirmation to the biomarkers in neuroimaging research because of fMRI pickup inherent connectivity between the brain and regions. There are profound studies in ASD with introducing machine learning or deep learning methods that have manifested advanced steps for ASD predictions based on fMRI data. However, utmost antecedent models have an inadequacy in their capacity to manipulate performance metrics such as accuracy, precision, recall, and F1-score. To overcome these problems, we proposed an avant-garde DarkASDNet, which has the competence to extract features from a lower level to a higher level and bring out promising results. In this work, we considered 3D fMRI data to predict binary classification between ASD and typical control (TC). Firstly, we pre-processed the 3D fMRI data by adopting proper slice time correction and normalization. Then, we introduced a novel DarkASDNet which surpassed the benchmark accuracy for the classification of ASD. Our model's outcomes unveil that our proposed method established state-of-the-art accuracy of 94.70% to classify ASD vs. TC in ABIDE-I, NYU dataset. Finally, we contemplated our model by performing evaluation metrics including precision, recall, F1-score, ROC curve, and AUC score, and legitimize by distinguishing with recent literature descriptions to vindicate our outcomes. The proposed DarkASDNet architecture provides a novel benchmark approach for ASD classification using fMRI processed data.
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Affiliation(s)
- Md Shale Ahammed
- Shandong Provincial Key Laboratory of Network Based Intelligent Computing, University of Jinan, Jinan, China
| | - Sijie Niu
- Shandong Provincial Key Laboratory of Network Based Intelligent Computing, University of Jinan, Jinan, China
| | | | - Jiwen Dong
- Shandong Provincial Key Laboratory of Network Based Intelligent Computing, University of Jinan, Jinan, China
| | - Xizhan Gao
- Shandong Provincial Key Laboratory of Network Based Intelligent Computing, University of Jinan, Jinan, China
| | - Yuehui Chen
- Shandong Provincial Key Laboratory of Network Based Intelligent Computing, University of Jinan, Jinan, China
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16
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Yao D, Sui J, Wang M, Yang E, Jiaerken Y, Luo N, Yap PT, Liu M, Shen D. A Mutual Multi-Scale Triplet Graph Convolutional Network for Classification of Brain Disorders Using Functional or Structural Connectivity. IEEE TRANSACTIONS ON MEDICAL IMAGING 2021; 40:1279-1289. [PMID: 33444133 PMCID: PMC8238125 DOI: 10.1109/tmi.2021.3051604] [Citation(s) in RCA: 48] [Impact Index Per Article: 16.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/20/2023]
Abstract
Brain connectivity alterations associated with mental disorders have been widely reported in both functional MRI (fMRI) and diffusion MRI (dMRI). However, extracting useful information from the vast amount of information afforded by brain networks remains a great challenge. Capturing network topology, graph convolutional networks (GCNs) have demonstrated to be superior in learning network representations tailored for identifying specific brain disorders. Existing graph construction techniques generally rely on a specific brain parcellation to define regions-of-interest (ROIs) to construct networks, often limiting the analysis into a single spatial scale. In addition, most methods focus on the pairwise relationships between the ROIs and ignore high-order associations between subjects. In this letter, we propose a mutual multi-scale triplet graph convolutional network (MMTGCN) to analyze functional and structural connectivity for brain disorder diagnosis. We first employ several templates with different scales of ROI parcellation to construct coarse-to-fine brain connectivity networks for each subject. Then, a triplet GCN (TGCN) module is developed to learn functional/structural representations of brain connectivity networks at each scale, with the triplet relationship among subjects explicitly incorporated into the learning process. Finally, we propose a template mutual learning strategy to train different scale TGCNs collaboratively for disease classification. Experimental results on 1,160 subjects from three datasets with fMRI or dMRI data demonstrate that our MMTGCN outperforms several state-of-the-art methods in identifying three types of brain disorders.
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17
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Chaitra N, Vijaya P, Deshpande G. Diagnostic prediction of autism spectrum disorder using complex network measures in a machine learning framework. Biomed Signal Process Control 2020. [DOI: 10.1016/j.bspc.2020.102099] [Citation(s) in RCA: 21] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/20/2022]
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18
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Ciarrusta J, Dimitrova R, McAlonan G. Early maturation of the social brain: How brain development provides a platform for the acquisition of social-cognitive competence. PROGRESS IN BRAIN RESEARCH 2020; 254:49-70. [PMID: 32859293 DOI: 10.1016/bs.pbr.2020.05.004] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Subscribe] [Scholar Register] [Indexed: 10/24/2022]
Abstract
Across the last century psychology has provided a lot of insight about social-cognitive competence. Recognizing facial expressions, joint attention, discrimination of cues and experiencing empathy are just a few examples of the social skills humans acquire from birth to adolescence. However, how very early brain maturation provides a platform to support the attainment of highly complex social behavior later in development remains poorly understood. Magnetic Resonance Imaging provides a safe means to investigate the typical and atypical maturation of regions of the brain responsible for social cognition in as early as the perinatal period. Here, we first review some technical challenges and advances of using functional magnetic resonance imaging on developing infants to then describe current knowledge on the development of diverse systems associated with social function. We will then explain how these characteristics might differ in infants with genetic or environmental risk factors, who are vulnerable to atypical neurodevelopment. Finally, given the rapid early development of systems necessary for social skills, we propose a new framework to investigate sensitive time windows of development when neural substrates might be more vulnerable to impairment due to a genetic or environmental insult.
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Affiliation(s)
- Judit Ciarrusta
- Centre for the Developing Brain, School Biomedical Engineering & Imaging Sciences, King's College London, St Thomas' Hospital, London, United Kingdom; Sackler Institute for Translational Neurodevelopment and Department of Forensic and Neurodevelopmental Sciences, Institute of Psychiatry, Psychology & Neuroscience, King's College London, London, United Kingdom
| | - Ralica Dimitrova
- Centre for the Developing Brain, School Biomedical Engineering & Imaging Sciences, King's College London, St Thomas' Hospital, London, United Kingdom; Sackler Institute for Translational Neurodevelopment and Department of Forensic and Neurodevelopmental Sciences, Institute of Psychiatry, Psychology & Neuroscience, King's College London, London, United Kingdom
| | - Grainne McAlonan
- Sackler Institute for Translational Neurodevelopment and Department of Forensic and Neurodevelopmental Sciences, Institute of Psychiatry, Psychology & Neuroscience, King's College London, London, United Kingdom; MRC Centre for Neurodevelopmental Disorders, King's College London, London, United Kingdom; South London and Maudsley NHS Foundation Trust, London, United Kingdom.
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19
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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.
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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.
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20
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Zhang L, Wang XH, Li L. Diagnosing autism spectrum disorder using brain entropy: A fast entropy method. COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2020; 190:105240. [PMID: 31806393 DOI: 10.1016/j.cmpb.2019.105240] [Citation(s) in RCA: 12] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/06/2019] [Revised: 11/07/2019] [Accepted: 11/25/2019] [Indexed: 06/10/2023]
Abstract
BACKGROUND AND OBJECTIVE Previous resting-state fMRI-based diagnostic models for autism spectrum disorder (ASD) were based on traditional linear features. The complexity of the ASD brain remains unexplored. METHODS To increase our understanding of the nonlinear neural mechanisms in ASD, entropy (i.e., approximate entropy (ApEn) and sample entropy (SampEn)) method was used to analyze the resting-state fMRI datasets collected from 21 ASD patients and 26 typically developing (TD) individuals. Here, a fast entropy algorithm was proposed through matrix computation. We combined entropy with a support-vector machine and selected "important entropy" as features to diagnose ASD. The classification performance of the fast entropy method was compared to the state-of-the-art functional connectivity (FC) method. RESULTS The area under the receiver operating characteristic curve based on FC was 0.62. The areas under the receiver operating characteristic curves based on ApEn and SampEn were 0.79 and 0.89, respectively. The results showed that the proposed fast entropy method was more efficacious than the FC method. In addition, lower entropy was found in the ASD patients. The ApEn of the left postcentral gyrus (rs = -0.556, p = 0.009) and the SampEn of the right lingual gyrus (rs = -0.526, p = 0.014) were both significantly negatively related to Autism Diagnostic Observation Schedule total scores in the ASD patients. The proposed algorithm for entropy computation was faster than the traditional entropy method. CONCLUSIONS Our study provides a new perspective to better understand the neural mechanisms of ASD. Brain entropy based on a fast algorithm was applied to distinguish ASD patients from TD individuals. ApEn and SampEn could be potential biomarkers in ASD investigations.
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Affiliation(s)
- Liangliang Zhang
- School of Computer Science and Technology, Hangzhou Dianzi University, Hangzhou 310018, China
| | - Xun-Heng Wang
- Institute of Biomedical Engineering and Instrumentation, School of Automation, Hangzhou Dianzi University, Hangzhou 310018, China
| | - Lihua Li
- School of Computer Science and Technology, Hangzhou Dianzi University, Hangzhou 310018, China; Institute of Biomedical Engineering and Instrumentation, School of Automation, Hangzhou Dianzi University, Hangzhou 310018, China.
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21
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Geng X, Kang X, Wong PCM. Autism spectrum disorder risk prediction: A systematic review of behavioral and neural investigations. PROGRESS IN MOLECULAR BIOLOGY AND TRANSLATIONAL SCIENCE 2020; 173:91-137. [PMID: 32711819 DOI: 10.1016/bs.pmbts.2020.04.015] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/16/2023]
Abstract
A reliable diagnosis of autism spectrum disorder (ASD) is difficult to make until after toddlerhood. Detection in an earlier age enables early intervention, which is typically more effective. Recent studies of the development of brain and behavior in infants and toddlers have provided important insights in the diagnosis of autism. This extensive review focuses on published studies of predicting the diagnosis of autism during infancy and toddlerhood younger than 3 years using behavioral and neuroimaging approaches. After screening a total of 782 papers, 17 neuroimaging and 43 behavioral studies were reviewed. The features for prediction consist of behavioral measures using screening tools, observational and experimental methods, brain volumetric measures, and neural functional activation and connectivity patterns. The classification approaches include logistic regression, linear discriminant function, decision trees, support vector machine, and deep learning based methods. Prediction performance has large variance across different studies. For behavioral studies, the sensitivity varies from 20% to 100%, and specificity ranges from 48% to 100%. The accuracy rates range from 61% to 94% in neuroimaging studies. Possible factors contributing to this inconsistency may be partially due to the heterogeneity of ASD, different targeted populations (i.e., high-risk group for ASD and general population), age when the features were collected, and validation procedures. The translation to clinical practice requires extensive further research including external validation with large sample size and optimized feature selection. The use of multi-modal features, e.g., combination of neuroimaging and behavior, is worth further investigation to improve the prediction accuracy.
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Affiliation(s)
- Xiujuan Geng
- Brain and Mind Institute, The Chinese University of Hong Kong, Shatin, Hong Kong.
| | - Xin Kang
- Brain and Mind Institute, The Chinese University of Hong Kong, Shatin, Hong Kong; Department of Linguistics and Modern Languages, The Chinese University of Hong Kong, Shatin, Hong Kong.
| | - Patrick C M Wong
- Brain and Mind Institute, The Chinese University of Hong Kong, Shatin, Hong Kong; Department of Linguistics and Modern Languages, The Chinese University of Hong Kong, Shatin, Hong Kong; Department of Otorhinolaryngology, Head and Neck Surgery, The Chinese University of Hong Kong, Shatin, Hong Kong
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22
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Zhao F, Chen Z, Rekik I, Lee SW, Shen D. Diagnosis of Autism Spectrum Disorder Using Central-Moment Features From Low- and High-Order Dynamic Resting-State Functional Connectivity Networks. Front Neurosci 2020; 14:258. [PMID: 32410930 PMCID: PMC7198826 DOI: 10.3389/fnins.2020.00258] [Citation(s) in RCA: 24] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/07/2019] [Accepted: 03/09/2020] [Indexed: 01/06/2023] Open
Abstract
The sliding-window-based dynamic functional connectivity networks (D-FCNs) derived from resting-state functional magnetic resonance imaging (rs-fMRI) are effective methods for diagnosing various neurological diseases, including autism spectrum disorder (ASD). However, traditional D-FCNs are low-order networks based on pairwise correlation between brain regions, thus overlooking high-level interactions across multiple regions of interest (ROIs). Moreover, D-FCNs suffer from the temporal mismatching issue, i.e., subnetworks in the same temporal window do not have temporal correspondence across different subjects. To address the above problems, we first construct a novel high-order D-FCNs based on the principle of “correlation’s correlation” to further explore the higher level and more complex interaction relationships among multiple ROIs. Furthermore, we propose to use a central-moment method to extract temporal-invariance properties contained in either low- or high-order D-FCNs. Finally, we design and train an ensemble classifier by fusing the features extracted from conventional FCN, low-order D-FCNs, and high-order D-FCNs for the diagnosis of ASD and normal control subjects. Our method achieved the best ASD classification accuracy (83%), and our results revealed the features extracted from different networks fingerprinting the autistic brain at different connectional levels.
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Affiliation(s)
- Feng Zhao
- School of Computer Science and Technology, Shandong Technology and Business University, Yantai, China.,Shandong Co-Innovation Center of Future Intelligent Computing, Yantai, China
| | - Zhiyuan Chen
- School of Computer Science and Technology, Shandong Technology and Business University, Yantai, China.,Shandong Co-Innovation Center of Future Intelligent Computing, Yantai, China
| | - Islem Rekik
- BASIRA Lab, CVIP Group, Computing, School of Science and Engineering, University of Dundee, Dundee, United Kingdom
| | - Seong-Whan Lee
- Department of Brain and Cognitive Engineering, Korea University, Seoul, South Korea
| | - Dinggang Shen
- Department of Radiology and Biomedical Research Imaging Central, University of North Carolina at Chapel Hill, Chapel Hill, NC, United States.,Department of Brain and Cognitive Engineering, Korea University, Seoul, South Korea
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23
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Adaptive sparse learning using multi-template for neurodegenerative disease diagnosis. Med Image Anal 2020; 61:101632. [PMID: 32028212 DOI: 10.1016/j.media.2019.101632] [Citation(s) in RCA: 20] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/27/2019] [Revised: 12/17/2019] [Accepted: 12/20/2019] [Indexed: 12/20/2022]
Abstract
Neurodegenerative diseases are excessively affecting millions of patients, especially elderly people. Early detection and management of these diseases are crucial as the clinical symptoms take years to appear after the onset of neuro-degeneration. This paper proposes an adaptive feature learning framework using multiple templates for early diagnosis. A multi-classification scheme is developed based on multiple brain parcellation atlases with various regions of interest. Different sets of features are extracted and then fused, and a feature selection is applied with an adaptively chosen sparse degree. In addition, both linear discriminative analysis and locally preserving projections are integrated to construct a least square regression model. Finally, we propose a feature space to predict the severity of the disease by the guidance of clinical scores. Our proposed method is validated on both Alzheimer's disease neuroimaging initiative and Parkinson's progression markers initiative databases. Extensive experimental results suggest that the proposed method outperforms the state-of-the-art methods, such as the multi-modal multi-task learning or joint sparse learning. Our method demonstrates that accurate feature learning facilitates the identification of the highly relevant brain regions with significant contribution in the prediction of disease progression. This may pave the way for further medical analysis and diagnosis in practical applications.
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24
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Attallah O, Sharkas MA, Gadelkarim H. Fetal Brain Abnormality Classification from MRI Images of Different Gestational Age. Brain Sci 2019; 9:brainsci9090231. [PMID: 31547368 PMCID: PMC6770437 DOI: 10.3390/brainsci9090231] [Citation(s) in RCA: 32] [Impact Index Per Article: 6.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/21/2019] [Revised: 08/30/2019] [Accepted: 09/08/2019] [Indexed: 02/07/2023] Open
Abstract
Magnetic resonance imaging (MRI) is a common imaging technique used extensively to study human brain activities. Recently, it has been used for scanning the fetal brain. Amongst 1000 pregnant women, 3 of them have fetuses with brain abnormality. Hence, the primary detection and classification are important. Machine learning techniques have a large potential in aiding the early detection of these abnormalities, which correspondingly could enhance the diagnosis process and follow up plans. Most research focused on the classification of abnormal brains in a primary age has been for newborns and premature infants, with fewer studies focusing on images for fetuses. These studies associated fetal scans to scans after birth for the detection and classification of brain defects early in the neonatal age. This type of brain abnormality is named small for gestational age (SGA). This article proposes a novel framework for the classification of fetal brains at an early age (before the fetus is born). As far as we could know, this is the first study to classify brain abnormalities of fetuses of widespread gestational ages (GAs). The study incorporates several machine learning classifiers, such as diagonal quadratic discriminates analysis (DQDA), K-nearest neighbour (K-NN), random forest, naïve Bayes, and radial basis function (RBF) neural network classifiers. Moreover, several bagging and Adaboosting ensembles models have been constructed using random forest, naïve Bayes, and RBF network classifiers. The performances of these ensembles have been compared with their individual models. Our results show that our novel approach can successfully identify and classify numerous types of defects within MRI images of the fetal brain of various GAs. Using the KNN classifier, we were able to achieve the highest classification accuracy and area under receiving operating characteristics of 95.6% and 99% respectively. In addition, ensemble classifiers improved the results of their respective individual models.
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Affiliation(s)
- Omneya Attallah
- Department of Electronics and Communications, College of Engineering and Technology, Arab Academy for Science and Technology and Maritime Transport, Alexandria, P.O. Box 1029, Egypt.
| | - Maha A Sharkas
- Department of Electronics and Communications, College of Engineering and Technology, Arab Academy for Science and Technology and Maritime Transport, Alexandria, P.O. Box 1029, Egypt.
| | - Heba Gadelkarim
- Department of Electronics and Communications, College of Engineering and Technology, Arab Academy for Science and Technology and Maritime Transport, Alexandria, P.O. Box 1029, Egypt.
- Department of Computer and Communication Engineering (SSP), Faculty of Engineering, Alexandria University, Alexandria 21526, Egypt.
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25
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Liu C, Zou L, Tang X, Zhu W, Zhang G, Qin Y, Zhu W. Changes of white matter integrity and structural network connectivity in nondemented cerebral small‐vessel disease. J Magn Reson Imaging 2019; 51:1162-1169. [PMID: 31448477 DOI: 10.1002/jmri.26906] [Citation(s) in RCA: 17] [Impact Index Per Article: 3.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/01/2019] [Revised: 08/11/2019] [Accepted: 08/12/2019] [Indexed: 01/10/2023] Open
Affiliation(s)
- Chengxia Liu
- Department of Radiology, Tongji Hospital, Tongji Medical CollegeHuazhong University of Science and Technology Wuhan China
| | - Lin Zou
- Department of Electrical and Electronic EngineeringSouthern University of Science and Technology Shenzhen Guangdong China
| | - Xiaoying Tang
- Department of Electrical and Electronic EngineeringSouthern University of Science and Technology Shenzhen Guangdong China
| | - Wenhao Zhu
- Department of Neurology, Tongji Hospital, Tongji Medical CollegeHuazhong University of Science and Technology Wuhan China
| | - Guiling Zhang
- Department of Radiology, Tongji Hospital, Tongji Medical CollegeHuazhong University of Science and Technology Wuhan China
| | - Yuanyuan Qin
- Department of Radiology, Tongji Hospital, Tongji Medical CollegeHuazhong University of Science and Technology Wuhan China
| | - Wenzhen Zhu
- Department of Radiology, Tongji Hospital, Tongji Medical CollegeHuazhong University of Science and Technology Wuhan China
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26
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Eill A, Jahedi A, Gao Y, Kohli JS, Fong CH, Solders S, Carper RA, Valafar F, Bailey BA, Müller RA. Functional Connectivities Are More Informative Than Anatomical Variables in Diagnostic Classification of Autism. Brain Connect 2019; 9:604-612. [PMID: 31328535 DOI: 10.1089/brain.2019.0689] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/15/2022] Open
Abstract
Machine learning techniques have been implemented to reveal brain features that distinguish people with autism spectrum disorders (ASDs) from typically developing (TD) peers. However, it remains unknown whether different neuroimaging modalities are equally informative for diagnostic classification. We combined anatomical magnetic resonance imaging (aMRI), diffusion weighted imaging (DWI), and functional connectivity MRI (fcMRI) using conditional random forest (CRF) for supervised learning to compare how informative each modality was in diagnostic classification. In-house data (N = 93) included 47 TD and 46 ASD participants, matched on age, motion, and nonverbal IQ. Four main analyses consistently indicated that fcMRI variables were significantly more informative than anatomical variables from aMRI and DWI. This was found (1) when the top 100 variables from CRF (run separately in each modality) were combined for multimodal CRF; (2) when only 19 top variables reaching >67% accuracy in each modality were combined in multimodal CRF; and (3) when the large number of initial variables (before dimension reduction) potentially biasing comparisons in favor of fcMRI was reduced using a less granular region of interest scheme. Consistent superiority of fcMRI was even found (4) when 100 variables per modality were randomly selected, removing any such potential bias. Greater informative value of functional than anatomical modalities may relate to the nature of fcMRI data, reflecting more closely behavioral condition, which is also the basis of diagnosis, whereas brain anatomy may be more reflective of neurodevelopmental history.
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Affiliation(s)
- Aina Eill
- Brain Development Imaging Laboratories, Department of Psychology, San Diego State University, San Diego, California.,Department of Bioinformatics and Medical Informatics, San Diego State University, San Diego, California
| | - Afrooz Jahedi
- Brain Development Imaging Laboratories, Department of Psychology, San Diego State University, San Diego, California.,Computational Science Research Center, San Diego State University, San Diego, California
| | - Yangfeifei Gao
- Brain Development Imaging Laboratories, Department of Psychology, San Diego State University, San Diego, California.,SDSU/UC San Diego Joint Doctoral Program in Clinical Psychology, San Diego, California
| | - Jiwandeep S Kohli
- Brain Development Imaging Laboratories, Department of Psychology, San Diego State University, San Diego, California.,SDSU/UC San Diego Joint Doctoral Program in Clinical Psychology, San Diego, California
| | - Christopher H Fong
- Brain Development Imaging Laboratories, Department of Psychology, San Diego State University, San Diego, California
| | - Seraphina Solders
- Brain Development Imaging Laboratories, Department of Psychology, San Diego State University, San Diego, California
| | - Ruth A Carper
- Brain Development Imaging Laboratories, Department of Psychology, San Diego State University, San Diego, California
| | - Faramarz Valafar
- Department of Bioinformatics and Medical Informatics, San Diego State University, San Diego, California
| | - Barbara A Bailey
- Department of Mathematics and Statistics, San Diego State University, San Diego, California
| | - Ralph-Axel Müller
- Brain Development Imaging Laboratories, Department of Psychology, San Diego State University, San Diego, California
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27
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Shi F, Salzwedel AP, Lin W, Gilmore JH, Gao W. Functional Brain Parcellations of the Infant Brain and the Associated Developmental Trends. Cereb Cortex 2019; 28:1358-1368. [PMID: 28334317 DOI: 10.1093/cercor/bhx062] [Citation(s) in RCA: 42] [Impact Index Per Article: 8.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/04/2017] [Indexed: 01/17/2023] Open
Abstract
Resting-state functional connectivity studies have dramatically improved our understanding of the early human brain functional development during the past decade. However, one emerging problem that could potentially impede future progresses in the field is the definition of regions of interest (ROI), since it is well known that functional connectivity estimation can be seriously contaminated by within-ROI signal heterogeneity. In this study, based on a large-scale rsfMRI data set in human infants (230 neonates, 143 1-year olds, and 107 2-year olds), we aimed to derive a set of anatomically constrained, infant-specific functional brain parcellations using functional connectivity-based clustering. Our results revealed significantly higher levels of signal homogeneity within the newly defined functional parcellations compared with other schemes. Importantly, the global functional connectivity patterns associated with the newly defined functional subunits demonstrated significantly increasing levels of differentiation with age, confirming increasing levels of local specialization. Subsequent whole brain connectivity analysis revealed intriguing patterns of regional-level functional connectivity developments and system-level hub redistribution during infancy. Overall, the newly derived infant-specific functional brain parcellations and the associated novel developmental patterns will likely prove valuable for future early developmental studies using the functional connectivity technique.
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Affiliation(s)
- Feng Shi
- Department of Biomedical Sciences and Imaging, Biomedical Imaging Research Institute (BIRI), Cedars-Sinai Medical Center, Los Angeles, CA 90048, USA
| | - Andrew P Salzwedel
- Department of Biomedical Sciences and Imaging, Biomedical Imaging Research Institute (BIRI), Cedars-Sinai Medical Center, Los Angeles, CA 90048, USA
| | - Weili Lin
- Department of Radiology, Biomedical Research Imaging Center, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA
| | - John H Gilmore
- Department of Psychiatry, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA
| | - Wei Gao
- Department of Biomedical Sciences and Imaging, Biomedical Imaging Research Institute (BIRI), Cedars-Sinai Medical Center, Los Angeles, CA 90048, USA
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28
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Payabvash S, Palacios EM, Owen JP, Wang MB, Tavassoli T, Gerdes M, Brandes-Aitken A, Marco EJ, Mukherjee P. Diffusion tensor tractography in children with sensory processing disorder: Potentials for devising machine learning classifiers. NEUROIMAGE-CLINICAL 2019; 23:101831. [PMID: 31035231 PMCID: PMC6488562 DOI: 10.1016/j.nicl.2019.101831] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 11/11/2018] [Revised: 03/22/2019] [Accepted: 04/18/2019] [Indexed: 12/11/2022]
Abstract
The "sensory processing disorder" (SPD) refers to brain's inability to organize sensory input for appropriate use. In this study, we determined the diffusion tensor imaging (DTI) microstructural and connectivity correlates of SPD, and apply machine learning algorithms for identification of children with SPD based on DTI/tractography metrics. A total of 44 children with SPD and 41 typically developing children (TDC) were prospectively recruited and scanned. In addition to fractional anisotropy (FA), mean diffusivity (MD), and radial diffusivity (RD), we applied probabilistic tractography to generate edge density (ED) and track density (TD) from DTI maps. For identification of children with SPD, accurate classification rates from a combination of DTI microstructural (FA, MD, AD, and RD), connectivity (TD) and connectomic (ED) metrics with different machine learning algorithms - including naïve Bayes, random forest, support vector machine, and neural networks - were determined. In voxel-wise analysis, children with SPD had lower FA, ED, and TD but higher MD and RD compared to TDC - predominantly in posterior white matter tracts including posterior corona radiata, posterior thalamic radiation, and posterior body and splenium of corpus callosum. In stepwise penalized logistic regression, the only independent variable distinguishing children with SPD from TDC was the average TD in the splenium (p < 0.001). Among different combinations of machine learning algorithms and DTI/connectivity metrics, random forest models using tract-based TD yielded the highest accuracy in classification of SPD - 77.5% accuracy, 73.8% sensitivity, and 81.6% specificity. Our findings demonstrate impaired microstructural and connectivity/connectomic integrity in children with SPD, predominantly in posterior white matter tracts, and with reduced TD of the splenium of corpus callosum as the most distinctive pattern. Applying machine learning algorithms, these connectivity metrics can be used to devise novel imaging biomarkers for neurodevelopmental disorders.
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Affiliation(s)
- Seyedmehdi Payabvash
- Department of Radiology and Biomedical Imaging, Yale School of Medicine, New Haven, CT, United States of America; Department of Radiology and Biomedical Imaging, University of California, San Francisco, CA, United States of America
| | - Eva M Palacios
- Department of Radiology and Biomedical Imaging, University of California, San Francisco, CA, United States of America
| | - Julia P Owen
- Department of Radiology, University of Washington, Seattle, WA, United States of America
| | - Maxwell B Wang
- Department of Radiology and Biomedical Imaging, University of California, San Francisco, CA, United States of America
| | - Teresa Tavassoli
- Department of Psychology and Clinical Sciences, University of Reading, Reading, United Kingdom
| | - Molly Gerdes
- Department of Neurology, University of California, San Francisco, CA, United States of America
| | - Anne Brandes-Aitken
- Department of Applied Psychology, New York University, New York, NY, United States of America
| | - Elysa J Marco
- Department of Neurology, University of California, San Francisco, CA, United States of America; Department of Pediatric Neurology, Cortica Healthcare, San Rafael, CA, United States of America
| | - Pratik Mukherjee
- Department of Radiology and Biomedical Imaging, University of California, San Francisco, CA, United States of America; Department of Bioengineering and Therapeutic Sciences, University of California, San Francisco, CA, United States of America.
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29
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Chen G, Zhang J, Zhang Y, Dong B, Shen D, Yap PT. Multi-channel framelet denoising of diffusion-weighted images. PLoS One 2019; 14:e0211621. [PMID: 30726257 PMCID: PMC6364918 DOI: 10.1371/journal.pone.0211621] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/29/2018] [Accepted: 01/17/2019] [Indexed: 11/18/2022] Open
Abstract
Diffusion MRI derives its contrast from MR signal attenuation induced by the movement of water molecules in microstructural environments. Associated with the signal attenuation is the reduction of signal-to-noise ratio (SNR). Methods based on total variation (TV) have shown superior performance in image noise reduction. However, TV denoising can result in stair-casing effects due to the inherent piecewise-constant assumption. In this paper, we propose a tight wavelet frame based approach for edge-preserving denoising of diffusion-weighted (DW) images. Specifically, we employ the unitary extension principle (UEP) to generate frames that are discrete analogues to differential operators of various orders, which will help avoid stair-casing effects. Instead of denoising each DW image separately, we collaboratively denoise groups of DW images acquired with adjacent gradient directions. In addition, we introduce a very efficient method for solving an ℓ0 denoising problem that involves only thresholding and solving a trivial inverse problem. We demonstrate the effectiveness of our method qualitatively and quantitatively using synthetic and real data.
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Affiliation(s)
- Geng Chen
- Department of Radiology and Biomedical Research Imaging Center (BRIC) University of North Carolina, Chapel Hill, United States of America
| | - Jian Zhang
- Department of Radiology and Biomedical Research Imaging Center (BRIC) University of North Carolina, Chapel Hill, United States of America
- School of Information and Electrical Engineering, Hunan University of Science & Technology, Xiangtan, China
| | - Yong Zhang
- Vancouver Research Center, Huawei, Burnaby, Canada
| | - Bin Dong
- Beijing International Center for Mathematical Research, Peking University, Beijing, China
| | - Dinggang Shen
- Department of Radiology and Biomedical Research Imaging Center (BRIC) University of North Carolina, Chapel Hill, United States of America
- Department of Brain and Cognitive Engineering, Korea University, Seoul, Korea
| | - Pew-Thian Yap
- Department of Radiology and Biomedical Research Imaging Center (BRIC) University of North Carolina, Chapel Hill, United States of America
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30
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Yin W, Chen MH, Hung SC, Baluyot KR, Li T, Lin W. Brain functional development separates into three distinct time periods in the first two years of life. Neuroimage 2019; 189:715-726. [PMID: 30641240 DOI: 10.1016/j.neuroimage.2019.01.025] [Citation(s) in RCA: 13] [Impact Index Per Article: 2.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/07/2018] [Revised: 12/25/2018] [Accepted: 01/09/2019] [Indexed: 12/14/2022] Open
Abstract
Recently, resting functional MRI has provided invaluable insight into the brain developmental processes of early infancy and childhood. A common feature of previous functional development studies is the use of age to separate subjects into different cohorts for group comparisons. However, functional maturation paces vary tremendously from subject to subject. Since this is particularly true for the first years of life, an alternative to physical age alone is needed for cluster analysis. Here, a data-driven approach based on individual brain functional connectivity was employed to cluster typically developing children who were longitudinally imaged using MRI without sedation for the first two years of life. Specifically, three time periods were determined based on the distinction of brain functional connectivity patterns, including 0-1 month (group 1), 2-7 months (group 2), and 8-24 (group 3) of age, respectively. From groups 1 to 2, connection density increased by almost two-fold, local efficacy (LE) is significantly improved, and there was no change in global efficiency (GE). From groups 2 to 3, connection density increased slightly, LE showed no change, and a significant increase in GE were observed. Furthermore, 27 core brain regions were identified which yielded clustering results that resemble those obtained using all brain regions. These core regions were largely associated with the motor, visual and language functional domains as well as regions associated with higher order cognitive functional domains. Both visual and language functional domains exhibited a persistent and significant increase within domain connection from groups 1 to 3, while no changes were observed for the motor domain. In contrast, while a reduction of inter-domain connection was the general developmental pattern, the motor domain exhibited an interesting "V" shape pattern in its relationship to visual and language associated areas, showing a decrease from groups 1 to 2, followed by an increase from groups 2 to 3. In summary, our results offer new insights into functional brain development and identify 27 core brain regions critically important for early brain development.
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Affiliation(s)
- Weiyan Yin
- Department of Biomedical Research Imaging Center, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
| | - Meng-Hsiang Chen
- Department of Diagnostic Radiology, Kaohsiung Chang Gung Memorial Hospital, Chang Gung University College of Medicine, Kaohsiung, Taiwan
| | - Sheng-Che Hung
- Department of Radiology, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
| | - Kristine R Baluyot
- Department of Biomedical Research Imaging Center, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
| | - Tengfei Li
- Department of Biomedical Research Imaging Center, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
| | - Weili Lin
- Department of Biomedical Research Imaging Center, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA; Department of Radiology, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA.
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31
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Graph theoretical modeling of baby brain networks. Neuroimage 2019; 185:711-727. [DOI: 10.1016/j.neuroimage.2018.06.038] [Citation(s) in RCA: 44] [Impact Index Per Article: 8.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/10/2017] [Revised: 05/22/2018] [Accepted: 06/11/2018] [Indexed: 11/20/2022] Open
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32
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Huang H, Liu X, Jin Y, Lee SW, Wee CY, Shen D. Enhancing the representation of functional connectivity networks by fusing multi-view information for autism spectrum disorder diagnosis. Hum Brain Mapp 2018; 40:833-854. [PMID: 30357998 DOI: 10.1002/hbm.24415] [Citation(s) in RCA: 31] [Impact Index Per Article: 5.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/17/2018] [Revised: 08/17/2018] [Accepted: 09/26/2018] [Indexed: 01/10/2023] Open
Abstract
Functional connectivity network provides novel insights on how distributed brain regions are functionally integrated, and its deviations from healthy brain have recently been employed to identify biomarkers for neuropsychiatric disorders. However, most of brain network analysis methods utilized features extracted only from one functional connectivity network for brain disease detection and cannot provide a comprehensive representation on the subtle disruptions of brain functional organization induced by neuropsychiatric disorders. Inspired by the principles of multi-view learning which utilizes information from multiple views to enhance object representation, we propose a novel multiple network based framework to enhance the representation of functional connectivity networks by fusing the common and complementary information conveyed in multiple networks. Specifically, four functional connectivity networks corresponding to the four adjacent values of regularization parameter are generated via a sparse regression model with group constraint ( l2,1 -norm), to enhance the common intrinsic topological structure and limit the error rate caused by different views. To obtain a set of more meaningful and discriminative features, we propose using a modified version of weighted clustering coefficients to quantify the subtle differences of each group-sparse network at local level. We then linearly fuse the selected features from each individual network via a multi-kernel support vector machine for autism spectrum disorder (ASD) diagnosis. The proposed framework achieves an accuracy of 79.35%, outperforming all the compared single network methods for at least 7% improvement. Moreover, compared with other multiple network methods, our method also achieves the best performance, that is, with at least 11% improvement in accuracy.
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Affiliation(s)
- Huifang Huang
- School of Computer and Information Technology, Beijing Jiaotong University, Beijing, China.,Biomedical Research Imaging Center (BRIC) and Department of Radiology, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina
| | - Xingdan Liu
- School of Computer and Information Technology, Beijing Jiaotong University, Beijing, China
| | - Yan Jin
- Biomedical Research Imaging Center (BRIC) and Department of Radiology, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina
| | - Seong-Whan Lee
- Department of Brain and Cognitive Engineering, Korea University, Seoul, Republic of Korea
| | - Chong-Yaw Wee
- Biomedical Research Imaging Center (BRIC) and Department of Radiology, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina.,Department of Biomedical Engineering, National University of Singapore, Singapore
| | - Dinggang Shen
- Biomedical Research Imaging Center (BRIC) and Department of Radiology, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina.,Department of Brain and Cognitive Engineering, Korea University, Seoul, Republic of Korea
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33
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Zhao F, Zhang H, Rekik I, An Z, Shen D. Diagnosis of Autism Spectrum Disorders Using Multi-Level High-Order Functional Networks Derived From Resting-State Functional MRI. Front Hum Neurosci 2018; 12:184. [PMID: 29867410 PMCID: PMC5960713 DOI: 10.3389/fnhum.2018.00184] [Citation(s) in RCA: 49] [Impact Index Per Article: 8.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/18/2018] [Accepted: 04/17/2018] [Indexed: 11/13/2022] Open
Abstract
Functional brain networks derived from resting-state functional magnetic resonance imaging (rs-fMRI) have been widely used for Autism Spectrum Disorder (ASD) diagnosis. Typically, these networks are constructed by calculating functional connectivity (FC) between any pair of brain regions of interest (ROIs), i.e., using Pearson's correlation between rs-fMRI time series. However, this can only be called as a low-order representation of the functional interaction, because the relationship is investigated just between two ROIs. Brain disorders might not only affect low-order FC, but also high-order FC, i.e., the higher-level relationship among multiple brain regions, which might be more crucial for diagnosis. To comprehensively characterize such relationship for better diagnosis of ASD, we propose a multi-level, high-order FC network representation that can nicely capture complex interactions among brain regions. Then, we design a feature selection method to identify those discriminative multi-level, high-order FC features for ASD diagnosis. Finally, we design an ensemble classifier with multiple linear SVMs, each trained on a specific level of FC networks, for boosting the final classification accuracy. Experimental results show that the integration of both low-order and first-level high-order FC networks achieves the best ASD diagnostic accuracy (81%). We further investigated those selected discriminative low-order and high-order FC features and found that the high-order FC features can provide complementary information to the low-order FC features in the ASD diagnosis.
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Affiliation(s)
- Feng Zhao
- School of Computer Science and Technology, Shandong Technology and Business University, Yantai, China
| | - Han Zhang
- Department of Radiology and BRIC, University of North Carolina, Chapel Hill, NC, United States
| | - Islem Rekik
- BASIRA Lab, CVIP Group, Computing, School of Science and Engineering, University of Dundee, Dundee, United Kingdom
| | - Zhiyong An
- School of Computer Science and Technology, Shandong Technology and Business University, Yantai, China
| | - Dinggang Shen
- Department of Radiology and BRIC, University of North Carolina, Chapel Hill, NC, United States.,Department of Brain and Cognitive Engineering, Korea University, Seoul, South Korea
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34
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Li G, Lee O, Rabitz H. High efficiency classification of children with autism spectrum disorder. PLoS One 2018; 13:e0192867. [PMID: 29447214 PMCID: PMC5814015 DOI: 10.1371/journal.pone.0192867] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/13/2017] [Accepted: 01/31/2018] [Indexed: 11/19/2022] Open
Abstract
Autism spectrum disorder (ASD) is a wide-ranging collection of developmental diseases with varying symptoms and degrees of disability. Currently, ASD is diagnosed mainly with psychometric tools, often unable to provide an early and reliable diagnosis. Recently, biochemical methods are being explored as a means to meet the latter need. For example, an increased predisposition to ASD has been associated with abnormalities of metabolites in folate-dependent one carbon metabolism (FOCM) and transsulfuration (TS). Multiple metabolites in the FOCM/TS pathways have been measured, and statistical analysis tools employed to identify certain metabolites that are closely related to ASD. The prime difficulty in such biochemical studies comes from (i) inefficient determination of which metabolites are most important and (ii) understanding how these metabolites are collectively related to ASD. This paper presents a new method based on scores produced in Support Vector Machine (SVM) modeling combined with High Dimensional Model Representation (HDMR) sensitivity analysis. The new method effectively and efficiently identifies the key causative metabolites in FOCM/TS pathways, ranks their importance, and discovers their independent and correlative action patterns upon ASD. Such information is valuable not only for providing a foundation for a pathological interpretation but also for potentially providing an early, reliable diagnosis ideally leading to a subsequent comprehensive treatment of ASD. With only tens of SVM model runs, the new method can identify the combinations of the most important metabolites in the FOCM/TS pathways that lead to ASD. Previous efforts to find these metabolites required hundreds of thousands of model runs with the same data.
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Affiliation(s)
- Genyuan Li
- Department of Chemistry, Princeton University, Princeton, New Jersey 08544, United States of America
| | - Olivia Lee
- Peddie School, Hightstown, New Jersey 08520, United States of America
| | - Herschel Rabitz
- Department of Chemistry, Princeton University, Princeton, New Jersey 08544, United States of America
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35
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Bi XA, Wang Y, Shu Q, Sun Q, Xu Q. Classification of Autism Spectrum Disorder Using Random Support Vector Machine Cluster. Front Genet 2018; 9:18. [PMID: 29467790 PMCID: PMC5808191 DOI: 10.3389/fgene.2018.00018] [Citation(s) in RCA: 47] [Impact Index Per Article: 7.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/16/2017] [Accepted: 01/15/2018] [Indexed: 01/03/2023] Open
Abstract
Autism spectrum disorder (ASD) is mainly reflected in the communication and language barriers, difficulties in social communication, and it is a kind of neurological developmental disorder. Most researches have used the machine learning method to classify patients and normal controls, among which support vector machines (SVM) are widely employed. But the classification accuracy of SVM is usually low, due to the usage of a single SVM as classifier. Thus, we used multiple SVMs to classify ASD patients and typical controls (TC). Resting-state functional magnetic resonance imaging (fMRI) data of 46 TC and 61 ASD patients were obtained from the Autism Brain Imaging Data Exchange (ABIDE) database. Only 84 of 107 subjects are utilized in experiments because the translation or rotation of 7 TC and 16 ASD patients has surpassed ±2 mm or ±2°. Then the random SVM cluster was proposed to distinguish TC and ASD. The results show that this method has an excellent classification performance based on all the features. Furthermore, the accuracy based on the optimal feature set could reach to 96.15%. Abnormal brain regions could also be found, such as inferior frontal gyrus (IFG) (orbital and opercula part), hippocampus, and precuneus. It is indicated that the method of random SVM cluster may apply to the auxiliary diagnosis of ASD.
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Affiliation(s)
- Xia-An Bi
- College of Mathematics and Computer Science, Hunan Normal University, Changsha, China
| | - Yang Wang
- College of Mathematics and Computer Science, Hunan Normal University, Changsha, China
| | - Qing Shu
- College of Mathematics and Computer Science, Hunan Normal University, Changsha, China
| | - Qi Sun
- College of Mathematics and Computer Science, Hunan Normal University, Changsha, China
| | - Qian Xu
- College of Mathematics and Computer Science, Hunan Normal University, Changsha, China
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36
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Wen H, Liu Y, Rekik I, Wang S, Zhang J, Zhang Y, Peng Y, He H. Disrupted topological organization of structural networks revealed by probabilistic diffusion tractography in Tourette syndrome children. Hum Brain Mapp 2017; 38:3988-4008. [PMID: 28474385 PMCID: PMC6866946 DOI: 10.1002/hbm.23643] [Citation(s) in RCA: 34] [Impact Index Per Article: 4.9] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/09/2016] [Revised: 04/17/2017] [Accepted: 04/24/2017] [Indexed: 01/18/2023] Open
Abstract
Tourette syndrome (TS) is a childhood-onset neurobehavioral disorder. Although previous TS studies revealed structural abnormalities in distinct corticobasal ganglia circuits, the topological alterations of the whole-brain white matter (WM) structural networks remain poorly understood. Here, we used diffusion MRI probabilistic tractography and graph theoretical analysis to investigate the topological organization of WM networks in 44 drug-naive TS children and 41 age- and gender-matched healthy children. The WM networks were constructed by estimating inter-regional connectivity probability and the topological properties were characterized using graph theory. We found that both TS and control groups showed an efficient small-world organization in WM networks. However, compared to controls, TS children exhibited decreased global and local efficiency, increased shortest path length and small worldness, indicating a disrupted balance between local specialization and global integration in structural networks. Although both TS and control groups showed highly similar hub distributions, TS children exhibited significant decreased nodal efficiency, mainly distributed in the default mode, language, visual, and sensorimotor systems. Furthermore, two separate networks showing significantly decreased connectivity in TS group were identified using network-based statistical (NBS) analysis, primarily composed of the parieto-occipital cortex, precuneus, and paracentral lobule. Importantly, we combined support vector machine and multiple kernel learning frameworks to fuse multiple levels of network topological features for classification of individuals, achieving high accuracy of 86.47%. Together, our study revealed the disrupted topological organization of structural networks related to pathophysiology of TS, and the discriminative topological features for classification are potential quantitative neuroimaging biomarkers for clinical TS diagnosis. Hum Brain Mapp 38:3988-4008, 2017. © 2017 Wiley Periodicals, Inc.
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Affiliation(s)
- Hongwei Wen
- Research Center for Brain‐inspired Intelligence, Institute of Automation, Chinese Academy of SciencesBeijingChina
- University of Chinese Academy of SciencesBeijingChina
| | - Yue Liu
- Department of RadiologyBeijing Children's Hospital, Capital Medical UniversityBeijingChina
| | - Islem Rekik
- CVIP, Computing, School of Science and EngineeringUniversity of DundeeUK
| | - Shengpei Wang
- Research Center for Brain‐inspired Intelligence, Institute of Automation, Chinese Academy of SciencesBeijingChina
- University of Chinese Academy of SciencesBeijingChina
| | - Jishui Zhang
- Department of NeurologyBeijing Children's Hospital, Capital Medical UniversityBeijingChina
| | - Yue Zhang
- Department of RadiologyBeijing Children's Hospital, Capital Medical UniversityBeijingChina
| | - Yun Peng
- Department of RadiologyBeijing Children's Hospital, Capital Medical UniversityBeijingChina
| | - Huiguang He
- Research Center for Brain‐inspired Intelligence, Institute of Automation, Chinese Academy of SciencesBeijingChina
- Center for Excellence in Brain Science and Intelligence Technology, Chinese Academy of SciencesBeijingChina
- University of Chinese Academy of SciencesBeijingChina
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37
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Zhao F, Qiao L, Shi F, Yap PT, Shen D. Feature fusion via hierarchical supervised local CCA for diagnosis of autism spectrum disorder. Brain Imaging Behav 2017; 11:1050-1060. [PMID: 27535871 PMCID: PMC5315688 DOI: 10.1007/s11682-016-9587-5] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/19/2023]
Abstract
Early diagnosis of autism spectrum disorder (ASD) is critical for timely medical intervention, for improving patient quality of life, and for reducing the financial burden borne by the society. A key issue in neuroimaging-based ASD diagnosis is the identification of discriminating features and then fusing them to produce accurate diagnosis. In this paper, we propose a novel framework for fusing complementary and discriminating features from different imaging modalities. Specifically, we integrate the Fisher discriminant criterion and local correlation information into the canonical correlation analysis (CCA) framework, giving a new feature fusion method, called Supervised Local CCA (SL-CCA), which caters specifically to local and global multimodal features. To alleviate the neighborhood selection problem associated with SL-CCA, we further propose a hierarchical SL-CCA (HSL-CCA), by performing SL-CCA with the gradually varying neighborhood sizes. Extensive experiments on the multimodal ABIDE database show that the proposed method achieves superior performance. In addition, based on feature weight analysis, we found that only a few specific brain regions play active roles in ASD diagnosis. These brain regions include the putamen, precuneus, and orbitofrontal cortex, which are highly associated with human emotional modulation and memory formation. These finding are consistent with the behavioral phenotype of ASD.
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Affiliation(s)
- Feng Zhao
- School of Computer Science and Technology, Shandong Institute of Business and Technology, YanTai, China
- Department of Radiology and BRIC, University of North Carolina at Chapel Hill, Carolina, North, USA
| | - Lishan Qiao
- School of Mathematics, Liaocheng University, Liaocheng, China
- Department of Radiology and BRIC, University of North Carolina at Chapel Hill, Carolina, North, USA
| | - Feng Shi
- Department of Radiology and BRIC, University of North Carolina at Chapel Hill, Carolina, North, USA
| | - Pew-Thian Yap
- Department of Radiology and BRIC, University of North Carolina at Chapel Hill, Carolina, North, USA
| | - Dinggang Shen
- Department of Radiology and BRIC, University of North Carolina at Chapel Hill, Carolina, North, USA.
- Department of Brain and Cognitive Engineering, Korea University, Seoul, 02841, Republic of Korea.
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38
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Chen X, Zhang H, Zhang L, Shen C, Lee SW, Shen D. Extraction of dynamic functional connectivity from brain grey matter and white matter for MCI classification. Hum Brain Mapp 2017; 38:5019-5034. [PMID: 28665045 DOI: 10.1002/hbm.23711] [Citation(s) in RCA: 95] [Impact Index Per Article: 13.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/24/2016] [Revised: 05/11/2017] [Accepted: 06/16/2017] [Indexed: 12/11/2022] Open
Abstract
Brain functional connectivity (FC) extracted from resting-state fMRI (RS-fMRI) has become a popular approach for diagnosing various neurodegenerative diseases, including Alzheimer's disease (AD) and its prodromal stage, mild cognitive impairment (MCI). Current studies mainly construct the FC networks between grey matter (GM) regions of the brain based on temporal co-variations of the blood oxygenation level-dependent (BOLD) signals, which reflects the synchronized neural activities. However, it was rarely investigated whether the FC detected within the white matter (WM) could provide useful information for diagnosis. Motivated by the recently proposed functional correlation tensors (FCT) computed from RS-fMRI and used to characterize the structured pattern of local FC in the WM, we propose in this article a novel MCI classification method based on the information conveyed by both the FC between the GM regions and that within the WM regions. Specifically, in the WM, the tensor-based metrics (e.g., fractional anisotropy [FA], similar to the metric calculated based on diffusion tensor imaging [DTI]) are first calculated based on the FCT and then summarized along each of the major WM fiber tracts connecting each pair of the brain GM regions. This could capture the functional information in the WM, in a similar network structure as the FC network constructed for the GM, based only on the same RS-fMRI data. Moreover, a sliding window approach is further used to partition the voxel-wise BOLD signal into multiple short overlapping segments. Then, both the FC and FCT between each pair of the brain regions can be calculated based on the BOLD signal segments in the GM and WM, respectively. In such a way, our method can generate dynamic FC and dynamic FCT to better capture functional information in both GM and WM and further integrate them together by using our developed feature extraction, selection, and ensemble learning algorithms. The experimental results verify that the dynamic FCT can provide valuable functional information in the WM; by combining it with the dynamic FC in the GM, the diagnosis accuracy for MCI subjects can be significantly improved even using RS-fMRI data alone. Hum Brain Mapp 38:5019-5034, 2017. © 2017 Wiley Periodicals, Inc.
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Affiliation(s)
- Xiaobo Chen
- Department of Radiology and BRIC, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina
| | - Han Zhang
- Department of Radiology and BRIC, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina
| | - Lichi Zhang
- Department of Radiology and BRIC, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina
| | - Celina Shen
- Department of Radiology and BRIC, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina
| | - Seong-Whan Lee
- Department of Brain and Cognitive Engineering, Korea University, Seoul, Republic of Korea
| | - Dinggang Shen
- Department of Radiology and BRIC, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina.,Department of Brain and Cognitive Engineering, Korea University, Seoul, Republic of Korea
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39
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Wang J, Wang Q, Peng J, Nie D, Zhao F, Kim M, Zhang H, Wee C, Wang S, Shen D. Multi-task diagnosis for autism spectrum disorders using multi-modality features: A multi-center study. Hum Brain Mapp 2017; 38:3081-3097. [PMID: 28345269 PMCID: PMC5427005 DOI: 10.1002/hbm.23575] [Citation(s) in RCA: 42] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/03/2016] [Revised: 12/22/2016] [Accepted: 03/08/2017] [Indexed: 01/11/2023] Open
Abstract
Autism spectrum disorder (ASD) is a neurodevelopment disease characterized by impairment of social interaction, language, behavior, and cognitive functions. Up to now, many imaging-based methods for ASD diagnosis have been developed. For example, one may extract abundant features from multi-modality images and then derive a discriminant function to map the selected features toward the disease label. A lot of recent works, however, are limited to single imaging centers. To this end, we propose a novel multi-modality multi-center classification (M3CC) method for ASD diagnosis. We treat the classification of each imaging center as one task. By introducing the task-task and modality-modality regularizations, we solve the classification for all imaging centers simultaneously. Meanwhile, the optimal feature selection and the modeling of the discriminant functions can be jointly conducted for highly accurate diagnosis. Besides, we also present an efficient iterative optimization solution to our formulated problem and further investigate its convergence. Our comprehensive experiments on the ABIDE database show that our proposed method can significantly improve the performance of ASD diagnosis, compared to the existing methods. Hum Brain Mapp 38:3081-3097, 2017. © 2017 Wiley Periodicals, Inc.
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Affiliation(s)
- Jun Wang
- School of Digital MediaJiangnan UniversityWuxiJiangsu214122China
- Department of Radiology and Biomedical Research Imaging CenterUniversity of North Carolina at Chapel HillChapel HillNorth Carolina27599
| | - Qian Wang
- Med‐X Research Institute, School of Biomedical Engineering, Shanghai Jiao Tong UniversityShanghaiChina
| | - Jialin Peng
- Department of Radiology and Biomedical Research Imaging CenterUniversity of North Carolina at Chapel HillChapel HillNorth Carolina27599
| | - Dong Nie
- Department of Radiology and Biomedical Research Imaging CenterUniversity of North Carolina at Chapel HillChapel HillNorth Carolina27599
| | - Feng Zhao
- Department of Radiology and Biomedical Research Imaging CenterUniversity of North Carolina at Chapel HillChapel HillNorth Carolina27599
| | - Minjeong Kim
- Department of Radiology and Biomedical Research Imaging CenterUniversity of North Carolina at Chapel HillChapel HillNorth Carolina27599
| | - Han Zhang
- Department of Radiology and Biomedical Research Imaging CenterUniversity of North Carolina at Chapel HillChapel HillNorth Carolina27599
| | - Chong‐Yaw Wee
- Department of Biomedical Engineering, Faculty of EngineeringNational University of SingaporeSingapore119077
| | - Shitong Wang
- School of Digital MediaJiangnan UniversityWuxiJiangsu214122China
| | - Dinggang Shen
- Department of Radiology and Biomedical Research Imaging CenterUniversity of North Carolina at Chapel HillChapel HillNorth Carolina27599
- Department of Brain and Cognitive EngineeringKorea UniversitySeoulKorea
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40
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Abstract
Noise in diffusion-weighted (DW) images increases the complexity of quantitative analysis and decreases the reliability of inferences. Hence, to improve analysis, it is often desirable to remove noise and at the same time preserve relevant image features. In this paper, we propose a tight wavelet frame based approach for edge-preserving denoising of DW images. Our approach (i) employs the unitary extension principle (UEP) to generate frames that are discrete analogues to differential operators of various orders; (ii) introduces a very efficient method for solving an ℓ0 denoising problem that involves only thresholding and solving a trivial inverse problem; and (iii) groups DW images acquired with neighboring gradient directions for collaborative denoising. Experiments using synthetic data with noncentral chi noise and real data with repeated scans confirm that our method yields superior performance compared with denoising using state-of-the-art methods such as non-local means.
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41
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Wen H, Liu Y, Rekik I, Wang S, Chen Z, Zhang J, Zhang Y, Peng Y, He H. Combining Disrupted and Discriminative Topological Properties of Functional Connectivity Networks as Neuroimaging Biomarkers for Accurate Diagnosis of Early Tourette Syndrome Children. Mol Neurobiol 2017; 55:3251-3269. [PMID: 28478510 DOI: 10.1007/s12035-017-0519-1] [Citation(s) in RCA: 33] [Impact Index Per Article: 4.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/08/2016] [Accepted: 04/06/2017] [Indexed: 01/18/2023]
Abstract
Tourette syndrome (TS) is a childhood-onset neurological disorder. To date, accurate TS diagnosis remains challenging due to its varied clinical expressions and dependency on qualitative description of symptoms. Therefore, identifying accurate and objective neuroimaging biomarkers may help improve early TS diagnosis. As resting-state functional MRI (rs-fMRI) has been demonstrated as a promising neuroimaging tool for TS diagnosis, previous rs-fMRI studies on TS revealed functional connectivity (FC) changes in a few local brain networks or circuits. However, no study explored the disrupted topological organization of whole-brain FC networks in TS children. Meanwhile, very few studies have examined brain functional networks using machine-learning methods for diagnostics. In this study, we construct individual whole-brain, ROI-level FC networks for 29 drug-naive TS children and 37 healthy children. Then, we use graph theory analysis to investigate the topological disruptions between groups. The identified disrupted regions in FC networks not only involved the sensorimotor association regions but also the visual, default-mode and language areas, all highly related to TS. Furthermore, we propose a novel classification framework based on similarity network fusion (SNF) algorithm, to both diagnose an individual subject and explore the discriminative power of FC network topological properties in distinguishing between TS children and controls. We achieved a high accuracy of 88.79%, and the involved discriminative regions for classification were also highly related to TS. Together, both the disrupted topological properties between groups and the discriminative topological features for classification may be considered as comprehensive and helpful neuroimaging biomarkers for assisting the clinical TS diagnosis.
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Affiliation(s)
- Hongwei Wen
- State Key Laboratory of Management and Control for Complex Systems, Institute of Automation, Chinese Academy of Sciences, Beijing, 100190, China.,Research Center for Brain-inspired Intelligence, Institute of Automation, Chinese Academy of Sciences, Beijing, China.,University of Chinese Academy of Sciences, Beijing, China
| | - Yue Liu
- Department of Radiology, Beijing Children's Hospital, Capital Medical University, No.56 Nanlishi Road, West District, Beijing, 100045, China
| | - Islem Rekik
- CVIP, Computing, School of Science and Engineering, University of Dundee, Dundee, UK
| | - Shengpei Wang
- State Key Laboratory of Management and Control for Complex Systems, Institute of Automation, Chinese Academy of Sciences, Beijing, 100190, China.,Research Center for Brain-inspired Intelligence, Institute of Automation, Chinese Academy of Sciences, Beijing, China.,University of Chinese Academy of Sciences, Beijing, China
| | - Zhiqiang Chen
- State Key Laboratory of Management and Control for Complex Systems, Institute of Automation, Chinese Academy of Sciences, Beijing, 100190, China.,Research Center for Brain-inspired Intelligence, Institute of Automation, Chinese Academy of Sciences, Beijing, China.,University of Chinese Academy of Sciences, Beijing, China
| | - Jishui Zhang
- Department of Neurology, Beijing Children's Hospital, Capital Medical University, Beijing, China
| | - Yue Zhang
- Department of Radiology, Beijing Children's Hospital, Capital Medical University, No.56 Nanlishi Road, West District, Beijing, 100045, China
| | - Yun Peng
- Department of Radiology, Beijing Children's Hospital, Capital Medical University, No.56 Nanlishi Road, West District, Beijing, 100045, China.
| | - Huiguang He
- State Key Laboratory of Management and Control for Complex Systems, Institute of Automation, Chinese Academy of Sciences, Beijing, 100190, China. .,Research Center for Brain-inspired Intelligence, Institute of Automation, Chinese Academy of Sciences, Beijing, China. .,University of Chinese Academy of Sciences, Beijing, China. .,Center for Excellence in Brain Science and Intelligence Technology, Chinese Academy of Sciences, Beijing, China.
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42
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Lei B, Yang P, Wang T, Chen S, Ni D. Relational-Regularized Discriminative Sparse Learning for Alzheimer's Disease Diagnosis. IEEE TRANSACTIONS ON CYBERNETICS 2017; 47:1102-1113. [PMID: 28092591 DOI: 10.1109/tcyb.2016.2644718] [Citation(s) in RCA: 38] [Impact Index Per Article: 5.4] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/06/2023]
Abstract
Accurate identification and understanding informative feature is important for early Alzheimer's disease (AD) prognosis and diagnosis. In this paper, we propose a novel discriminative sparse learning method with relational regularization to jointly predict the clinical score and classify AD disease stages using multimodal features. Specifically, we apply a discriminative learning technique to expand the class-specific difference and include geometric information for effective feature selection. In addition, two kind of relational information are incorporated to explore the intrinsic relationships among features and training subjects in terms of similarity learning. We map the original feature into the target space to identify the informative and predictive features by sparse learning technique. A unique loss function is designed to include both discriminative learning and relational regularization methods. Experimental results based on a total of 805 subjects [including 226 AD patients, 393 mild cognitive impairment (MCI) subjects, and 186 normal controls (NCs)] from AD neuroimaging initiative database show that the proposed method can obtain a classification accuracy of 94.68% for AD versus NC, 80.32% for MCI versus NC, and 74.58% for progressive MCI versus stable MCI, respectively. In addition, we achieve remarkable performance for the clinical scores prediction and classification label identification, which has efficacy for AD disease diagnosis and prognosis. The algorithm comparison demonstrates the effectiveness of the introduced learning techniques and superiority over the state-of-the-arts methods.
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43
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Candidate Biomarkers in Children with Autism Spectrum Disorder: A Review of MRI Studies. Neurosci Bull 2017; 33:219-237. [PMID: 28283808 PMCID: PMC5360855 DOI: 10.1007/s12264-017-0118-1] [Citation(s) in RCA: 78] [Impact Index Per Article: 11.1] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/08/2017] [Accepted: 02/17/2017] [Indexed: 11/25/2022] Open
Abstract
Searching for effective biomarkers is one of the most challenging tasks in the research field of Autism Spectrum Disorder (ASD). Magnetic resonance imaging (MRI) provides a non-invasive and powerful tool for investigating changes in the structure, function, maturation, connectivity, and metabolism of the brain of children with ASD. Here, we review the more recent MRI studies in young children with ASD, aiming to provide candidate biomarkers for the diagnosis of childhood ASD. The review covers structural imaging methods, diffusion tensor imaging, resting-state functional MRI, and magnetic resonance spectroscopy. Future advances in neuroimaging techniques, as well as cross-disciplinary studies and large-scale collaborations will be needed for an integrated approach linking neuroimaging, genetics, and phenotypic data to allow the discovery of new, effective biomarkers.
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44
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Multi-Class Disease Classification in Brain MRIs Using a Computer-Aided Diagnostic System. Symmetry (Basel) 2017. [DOI: 10.3390/sym9030037] [Citation(s) in RCA: 19] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/16/2022] Open
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45
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Dean DC, Lange N, Travers BG, Prigge MB, Matsunami N, Kellett KA, Freeman A, Kane KL, Adluru N, Tromp DPM, Destiche DJ, Samsin D, Zielinski BA, Fletcher PT, Anderson JS, Froehlich AL, Leppert MF, Bigler ED, Lainhart JE, Alexander AL. Multivariate characterization of white matter heterogeneity in autism spectrum disorder. Neuroimage Clin 2017; 14:54-66. [PMID: 28138427 PMCID: PMC5257193 DOI: 10.1016/j.nicl.2017.01.002] [Citation(s) in RCA: 13] [Impact Index Per Article: 1.9] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/01/2016] [Revised: 12/21/2016] [Accepted: 01/03/2017] [Indexed: 12/20/2022]
Abstract
The complexity and heterogeneity of neuroimaging findings in individuals with autism spectrum disorder has suggested that many of the underlying alterations are subtle and involve many brain regions and networks. The ability to account for multivariate brain features and identify neuroimaging measures that can be used to characterize individual variation have thus become increasingly important for interpreting and understanding the neurobiological mechanisms of autism. In the present study, we utilize the Mahalanobis distance, a multidimensional counterpart of the Euclidean distance, as an informative index to characterize individual brain variation and deviation in autism. Longitudinal diffusion tensor imaging data from 149 participants (92 diagnosed with autism spectrum disorder and 57 typically developing controls) between 3.1 and 36.83 years of age were acquired over a roughly 10-year period and used to construct the Mahalanobis distance from regional measures of white matter microstructure. Mahalanobis distances were significantly greater and more variable in the autistic individuals as compared to control participants, demonstrating increased atypicalities and variation in the group of individuals diagnosed with autism spectrum disorder. Distributions of multivariate measures were also found to provide greater discrimination and more sensitive delineation between autistic and typically developing individuals than conventional univariate measures, while also being significantly associated with observed traits of the autism group. These results help substantiate autism as a truly heterogeneous neurodevelopmental disorder, while also suggesting that collectively considering neuroimaging measures from multiple brain regions provides improved insight into the diversity of brain measures in autism that is not observed when considering the same regions separately. Distinguishing multidimensional brain relationships may thus be informative for identifying neuroimaging-based phenotypes, as well as help elucidate underlying neural mechanisms of brain variation in autism spectrum disorders.
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Affiliation(s)
- D C Dean
- Waisman Center, University of Wisconsin-Madison, Madison, WI, USA
| | - N Lange
- Department of Psychiatry, Harvard School of Medicine, Boston, MA, USA; Child and Adolescent Psychiatry, McLean Hospital, Belmont, MA, USA
| | - B G Travers
- Waisman Center, University of Wisconsin-Madison, Madison, WI, USA; Occupational Therapy Program, Department of Kinesiology, University of Wisconsin-Madison, Madison, WI, USA
| | - M B Prigge
- Department of Radiology, University of Utah, Salt Lake City, UT, USA; Department of Pediatrics, University of Utah and Primary Children's Medical Center, Salt Lake City, UT, USA
| | - N Matsunami
- Department of Human Genetics, University of Utah, Salt Lake City, UT, USA
| | - K A Kellett
- Waisman Center, University of Wisconsin-Madison, Madison, WI, USA; Department of Psychology, University of Wisconsin-Madison, Madison, WI, USA
| | - A Freeman
- Waisman Center, University of Wisconsin-Madison, Madison, WI, USA
| | - K L Kane
- Waisman Center, University of Wisconsin-Madison, Madison, WI, USA
| | - N Adluru
- Waisman Center, University of Wisconsin-Madison, Madison, WI, USA
| | - D P M Tromp
- Waisman Center, University of Wisconsin-Madison, Madison, WI, USA; Department of Psychiatry, University of Wisconsin-Madison, Madison, WI, USA
| | - D J Destiche
- Waisman Center, University of Wisconsin-Madison, Madison, WI, USA
| | - D Samsin
- Waisman Center, University of Wisconsin-Madison, Madison, WI, USA
| | - B A Zielinski
- Department of Pediatrics, University of Utah and Primary Children's Medical Center, Salt Lake City, UT, USA; Department of Neurology, University of Utah, Salt Lake City, UT, USA
| | - P T Fletcher
- Scientific Computing and Imaging Institute, University of Utah, Salt Lake City, UT, USA; School of Computing, University of Utah, Salt Lake City, UT, USA
| | - J S Anderson
- Department of Radiology, University of Utah, Salt Lake City, UT, USA; Interdepartmental Program in Neuroscience, University of Utah, Salt Lake City, UT, USA
| | - A L Froehlich
- School of Computing, University of Utah, Salt Lake City, UT, USA
| | - M F Leppert
- Department of Human Genetics, University of Utah, Salt Lake City, UT, USA
| | - E D Bigler
- Department of Psychology, Brigham Young University, Provo, UT, USA; Neuroscience Center, Brigham Young University, Provo, UT 84602, USA
| | - J E Lainhart
- Waisman Center, University of Wisconsin-Madison, Madison, WI, USA; Department of Psychiatry, University of Wisconsin-Madison, Madison, WI, USA
| | - A L Alexander
- Waisman Center, University of Wisconsin-Madison, Madison, WI, USA; Department of Psychiatry, University of Wisconsin-Madison, Madison, WI, USA; Department of Medical Physics, University of Wisconsin-Madison, Madison, WI, USA
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46
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Jin Y, Huang C, Daianu M, Zhan L, Dennis EL, Reid RI, Jack CR, Zhu H, Thompson PM. 3D tract-specific local and global analysis of white matter integrity in Alzheimer's disease. Hum Brain Mapp 2016; 38:1191-1207. [PMID: 27883250 PMCID: PMC5299040 DOI: 10.1002/hbm.23448] [Citation(s) in RCA: 32] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/28/2016] [Revised: 10/13/2016] [Accepted: 10/13/2016] [Indexed: 12/04/2022] Open
Abstract
Alzheimer's disease (AD) is a chronic neurodegenerative disease characterized by progressive decline in memory and other aspects of cognitive function. Diffusion‐weighted imaging (DWI) offers a non‐invasive approach to delineate the effects of AD on white matter (WM) integrity. Previous studies calculated either some summary statistics over regions of interest (ROI analysis) or some statistics along mean skeleton lines (Tract Based Spatial Statistic [TBSS]), so they cannot quantify subtle local WM alterations along major tracts. Here, a comprehensive WM analysis framework to map disease effects on 3D tracts both locally and globally, based on a study of 200 subjects: 49 healthy elderly normal controls, 110 with mild cognitive impairment, and 41 AD patients has been presented. 18 major WM tracts were extracted with our automated clustering algorithm—autoMATE (automated Multi‐Atlas Tract Extraction); we then extracted multiple DWI‐derived parameters of WM integrity along the WM tracts across all subjects. A novel statistical functional analysis method—FADTTS (Functional Analysis for Diffusion Tensor Tract Statistics) was applied to quantify degenerative patterns along WM tracts across different stages of AD. Gradually increasing WM alterations were found in all tracts in successive stages of AD. Among all 18 WM tracts, the fornix was most adversely affected. Among all the parameters, mean diffusivity (MD) was the most sensitive to WM alterations in AD. This study provides a systematic workflow to examine WM integrity across automatically computed 3D tracts in AD and may be useful in studying other neurological and psychiatric disorders. Hum Brain Mapp 38:1191–1207, 2017. © 2016 Wiley Periodicals, Inc.
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Affiliation(s)
- Yan Jin
- Imaging Genetics Center, Mark and Mary Stevens Neuroimaging and Informatics Institute, Keck School of Medicine, University of Southern California, Marina del Rey, California.,Department of Biostatistics and Biomedical Research Imaging Center, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina.,Department of Biostatistics, University of Texas MD Anderson Cancer Center, Houston, Texas
| | - Chao Huang
- Department of Biostatistics and Biomedical Research Imaging Center, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina
| | - Madelaine Daianu
- Imaging Genetics Center, Mark and Mary Stevens Neuroimaging and Informatics Institute, Keck School of Medicine, University of Southern California, Marina del Rey, California
| | - Liang Zhan
- Imaging Genetics Center, Mark and Mary Stevens Neuroimaging and Informatics Institute, Keck School of Medicine, University of Southern California, Marina del Rey, California.,Computer Engineering Program, University of Wisconsin-Stout, Menomonie, Wisconsin
| | - Emily L Dennis
- Imaging Genetics Center, Mark and Mary Stevens Neuroimaging and Informatics Institute, Keck School of Medicine, University of Southern California, Marina del Rey, California
| | - Robert I Reid
- Department of Information Technology, Mayo Clinic, Rochester, Minnesota
| | | | - Hongtu Zhu
- Department of Biostatistics and Biomedical Research Imaging Center, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina.,Department of Biostatistics, University of Texas MD Anderson Cancer Center, Houston, Texas
| | - Paul M Thompson
- Imaging Genetics Center, Mark and Mary Stevens Neuroimaging and Informatics Institute, Keck School of Medicine, University of Southern California, Marina del Rey, California.,Departments of Neurology, Psychiatry, Pediatrics, Radiology, and Ophthalmology, Keck School of Medicine, University of Southern California, Los Angeles, California.,Viterbi School of Engineering, University of Southern California, Los Angeles, California
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47
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Peng J, An L, Zhu X, Jin Y, Shen D. Structured Sparse Kernel Learning for Imaging Genetics Based Alzheimer's Disease Diagnosis. MEDICAL IMAGE COMPUTING AND COMPUTER-ASSISTED INTERVENTION : MICCAI ... INTERNATIONAL CONFERENCE ON MEDICAL IMAGE COMPUTING AND COMPUTER-ASSISTED INTERVENTION 2016; 9901:70-78. [PMID: 28580458 DOI: 10.1007/978-3-319-46723-8_9] [Citation(s) in RCA: 16] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/11/2022]
Abstract
A kernel-learning based method is proposed to integrate multimodal imaging and genetic data for Alzheimer's disease (AD) diagnosis. To facilitate structured feature learning in kernel space, we represent each feature with a kernel and then group kernels according to modalities. In view of the highly redundant features within each modality and also the complementary information across modalities, we introduce a novel structured sparsity regularizer for feature selection and fusion, which is different from conventional lasso and group lasso based methods. Specifically, we enforce a penalty on kernel weights to simultaneously select features sparsely within each modality and densely combine different modalities. We have evaluated the proposed method using magnetic resonance imaging (MRI) and positron emission tomography (PET), and single-nucleotide polymorphism (SNP) data of subjects from Alzheimer's Disease Neuroimaging Initiative (ADNI) database. The effectiveness of our method is demonstrated by both the clearly improved prediction accuracy and the discovered brain regions and SNPs relevant to AD.
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Affiliation(s)
- Jailin Peng
- Department of Radiology and BRIC, UNC at Chapel Hill, Chapel Hill, NC, USA.,College of Computer Science and Technology, Huaqiao University, Xiamen, China
| | - Le An
- Department of Radiology and BRIC, UNC at Chapel Hill, Chapel Hill, NC, USA
| | - Xiaofeng Zhu
- Department of Radiology and BRIC, UNC at Chapel Hill, Chapel Hill, NC, USA
| | - Yan Jin
- Department of Radiology and BRIC, UNC at Chapel Hill, Chapel Hill, NC, USA
| | - Dinggang Shen
- Department of Radiology and BRIC, UNC at Chapel Hill, Chapel Hill, NC, USA
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48
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Thung KH, Adeli E, Yap PT, Shen D. Stability-Weighted Matrix Completion of Incomplete Multi-modal Data for Disease Diagnosis. MEDICAL IMAGE COMPUTING AND COMPUTER-ASSISTED INTERVENTION : MICCAI ... INTERNATIONAL CONFERENCE ON MEDICAL IMAGE COMPUTING AND COMPUTER-ASSISTED INTERVENTION 2016; 9901:88-96. [PMID: 28286884 PMCID: PMC5343765 DOI: 10.1007/978-3-319-46723-8_11] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/16/2023]
Abstract
Effective utilization of heterogeneous multi-modal data for Alzheimer's Disease (AD) diagnosis and prognosis has always been hampered by incomplete data. One method to deal with this is low-rank matrix completion (LRMC), which simultaneous imputes missing data features and target values of interest. Although LRMC yields reasonable results, it implicitly weights features from all the modalities equally, ignoring the differences in discriminative power of features from different modalities. In this paper, we propose stability-weighted LRMC (swLRMC), an LRMC improvement that weights features and modalities according to their importance and reliability. We introduce a method, called stability weighting, to utilize subsampling techniques and outcomes from a range of hyper-parameters of sparse feature learning to obtain a stable set of weights. Incorporating these weights into LRMC, swLRMC can better account for differences in features and modalities for improving diagnosis. Experimental results confirm that the proposed method outperforms the conventional LRMC, feature-selection based LRMC, and other state-of-the-art methods.
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Affiliation(s)
- Kim-Han Thung
- Department of Radiology and BRIC, University of North Carolina, Chapel Hill, USA
| | - Ehsan Adeli
- Department of Radiology and BRIC, University of North Carolina, Chapel Hill, USA
| | - Pew-Thian Yap
- Department of Radiology and BRIC, University of North Carolina, Chapel Hill, USA
| | - Dinggang Shen
- Department of Radiology and BRIC, University of North Carolina, Chapel Hill, USA
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49
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Zhu X, Suk HI, Huang H, Shen D. Structured Sparse Low-Rank Regression Model for Brain-Wide and Genome-Wide Associations. MEDICAL IMAGE COMPUTING AND COMPUTER-ASSISTED INTERVENTION : MICCAI ... INTERNATIONAL CONFERENCE ON MEDICAL IMAGE COMPUTING AND COMPUTER-ASSISTED INTERVENTION 2016; 9900:344-352. [PMID: 28530001 PMCID: PMC5436308 DOI: 10.1007/978-3-319-46720-7_40] [Citation(s) in RCA: 12] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/24/2022]
Abstract
With the advances of neuroimaging techniques and genome sequences understanding, the phenotype and genotype data have been utilized to study the brain diseases (known as imaging genetics). One of the most important topics in image genetics is to discover the genetic basis of phenotypic markers and their associations. In such studies, the linear regression models have been playing an important role by providing interpretable results. However, due to their modeling characteristics, it is limited to effectively utilize inherent information among the phenotypes and genotypes, which are helpful for better understanding their associations. In this work, we propose a structured sparse low-rank regression method to explicitly consider the correlations within the imaging phenotypes and the genotypes simultaneously for Brain-Wide and Genome-Wide Association (BW-GWA) study. Specifically, we impose the low-rank constraint as well as the structured sparse constraint on both phenotypes and phenotypes. By using the Alzheimer's Disease Neuroimaging Initiative (ADNI) dataset, we conducted experiments of predicting the phenotype data from genotype data and achieved performance improvement by 12.75 % on average in terms of the root-mean-square error over the state-of-the-art methods.
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Affiliation(s)
- Xiaofeng Zhu
- Department of Radiology and BRIC, University of North Carolina at Chapel Hill, Chapel Hill, USA
| | - Heung-Il Suk
- Department of Brain and Cognitive Engineering, Korea University, Seoul, Republic of Korea
| | - Heng Huang
- Computer Science and Engineering, University of Texas at Arlington, Arlington, USA
| | - Dinggang Shen
- Department of Radiology and BRIC, University of North Carolina at Chapel Hill, Chapel Hill, USA
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50
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Multi-Tissue Decomposition of Diffusion MRI Signals via Sparse-Group Estimation. IEEE TRANSACTIONS ON IMAGE PROCESSING : A PUBLICATION OF THE IEEE SIGNAL PROCESSING SOCIETY 2016; 25:4340-4353. [PMID: 27392357 PMCID: PMC5219847 DOI: 10.1109/tip.2016.2588328] [Citation(s) in RCA: 15] [Impact Index Per Article: 1.9] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/28/2023]
Abstract
Sparse estimation techniques are widely utilized in diffusion magnetic resonance imaging (DMRI). In this paper, we present an algorithm for solving the ℓ0 sparse-group estimation problem and apply it to the tissue signal separation problem in DMRI. Our algorithm solves the ℓ0 problem directly, unlike existing approaches that often seek to solve its relaxed approximations. We include the mathematical proofs showing that the algorithm will converge to a solution satisfying the firstorder optimality condition within a finite number of iterations. We apply this algorithm to DMRI data to tease apart signal contributions from white matter, gray matter, and cerebrospinal fluid with the aim of improving the estimation of the fiber orientation distribution function (FODF). Unlike spherical deconvolution approaches that assume an invariant fiber response function (RF), our approach utilizes an RF group to span the signal subspace of each tissue type, allowing greater flexibility in accounting for possible variations of the RF throughout space and within each voxel. Our ℓ0 algorithm allows for the natural groupings of the RFs to be considered during signal decomposition. Experimental results confirm that our method yields estimates of FODFs and volume fractions of tissue compartments with improved robustness and accuracy. Our ℓ0 algorithm is general and can be applied to sparse estimation problems beyond the scope of this paper.
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