Using deep autoencoders to identify abnormal brain structural patterns in neuropsychiatric disorders: A large‐scale multi‐sample study
Machine learning is becoming an increasingly popular approach for investigating spatially distributed and subtle neuroanatomical alterations in brain‐based disorders. However, some machine learning models have been criticized for requiring a large number of cases in each experimental group, and for...
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| Veröffentlicht in: | Human brain mapping Jg. 40; H. 3; S. 944 - 954 |
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Hoboken, USA
John Wiley & Sons, Inc
15.02.2019
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| ISSN: | 1065-9471, 1097-0193, 1097-0193 |
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| Abstract | Machine learning is becoming an increasingly popular approach for investigating spatially distributed and subtle neuroanatomical alterations in brain‐based disorders. However, some machine learning models have been criticized for requiring a large number of cases in each experimental group, and for resembling a “black box” that provides little or no insight into the nature of the data. In this article, we propose an alternative conceptual and practical approach for investigating brain‐based disorders which aim to overcome these limitations. We used an artificial neural network known as “deep autoencoder” to create a normative model using structural magnetic resonance imaging data from 1,113 healthy people. We then used this model to estimate total and regional neuroanatomical deviation in individual patients with schizophrenia and autism spectrum disorder using two independent data sets (n = 263). We report that the model was able to generate different values of total neuroanatomical deviation for each disease under investigation relative to their control group (p < .005). Furthermore, the model revealed distinct patterns of neuroanatomical deviations for the two diseases, consistent with the existing neuroimaging literature. We conclude that the deep autoencoder provides a flexible and promising framework for assessing total and regional neuroanatomical deviations in neuropsychiatric populations. |
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| AbstractList | Machine learning is becoming an increasingly popular approach for investigating spatially distributed and subtle neuroanatomical alterations in brain‐based disorders. However, some machine learning models have been criticized for requiring a large number of cases in each experimental group, and for resembling a “black box” that provides little or no insight into the nature of the data. In this article, we propose an alternative conceptual and practical approach for investigating brain‐based disorders which aim to overcome these limitations. We used an artificial neural network known as “deep autoencoder” to create a normative model using structural magnetic resonance imaging data from 1,113 healthy people. We then used this model to estimate total and regional neuroanatomical deviation in individual patients with schizophrenia and autism spectrum disorder using two independent data sets (n = 263). We report that the model was able to generate different values of total neuroanatomical deviation for each disease under investigation relative to their control group (p < .005). Furthermore, the model revealed distinct patterns of neuroanatomical deviations for the two diseases, consistent with the existing neuroimaging literature. We conclude that the deep autoencoder provides a flexible and promising framework for assessing total and regional neuroanatomical deviations in neuropsychiatric populations. Machine learning is becoming an increasingly popular approach for investigating spatially distributed and subtle neuroanatomical alterations in brain-based disorders. However, some machine learning models have been criticized for requiring a large number of cases in each experimental group, and for resembling a "black box" that provides little or no insight into the nature of the data. In this article, we propose an alternative conceptual and practical approach for investigating brain-based disorders which aim to overcome these limitations. We used an artificial neural network known as "deep autoencoder" to create a normative model using structural magnetic resonance imaging data from 1,113 healthy people. We then used this model to estimate total and regional neuroanatomical deviation in individual patients with schizophrenia and autism spectrum disorder using two independent data sets (n = 263). We report that the model was able to generate different values of total neuroanatomical deviation for each disease under investigation relative to their control group (p < .005). Furthermore, the model revealed distinct patterns of neuroanatomical deviations for the two diseases, consistent with the existing neuroimaging literature. We conclude that the deep autoencoder provides a flexible and promising framework for assessing total and regional neuroanatomical deviations in neuropsychiatric populations.Machine learning is becoming an increasingly popular approach for investigating spatially distributed and subtle neuroanatomical alterations in brain-based disorders. However, some machine learning models have been criticized for requiring a large number of cases in each experimental group, and for resembling a "black box" that provides little or no insight into the nature of the data. In this article, we propose an alternative conceptual and practical approach for investigating brain-based disorders which aim to overcome these limitations. We used an artificial neural network known as "deep autoencoder" to create a normative model using structural magnetic resonance imaging data from 1,113 healthy people. We then used this model to estimate total and regional neuroanatomical deviation in individual patients with schizophrenia and autism spectrum disorder using two independent data sets (n = 263). We report that the model was able to generate different values of total neuroanatomical deviation for each disease under investigation relative to their control group (p < .005). Furthermore, the model revealed distinct patterns of neuroanatomical deviations for the two diseases, consistent with the existing neuroimaging literature. We conclude that the deep autoencoder provides a flexible and promising framework for assessing total and regional neuroanatomical deviations in neuropsychiatric populations. Machine learning is becoming an increasingly popular approach for investigating spatially distributed and subtle neuroanatomical alterations in brain‐based disorders. However, some machine learning models have been criticized for requiring a large number of cases in each experimental group, and for resembling a “black box” that provides little or no insight into the nature of the data. In this article, we propose an alternative conceptual and practical approach for investigating brain‐based disorders which aim to overcome these limitations. We used an artificial neural network known as “deep autoencoder” to create a normative model using structural magnetic resonance imaging data from 1,113 healthy people. We then used this model to estimate total and regional neuroanatomical deviation in individual patients with schizophrenia and autism spectrum disorder using two independent data sets ( n = 263). We report that the model was able to generate different values of total neuroanatomical deviation for each disease under investigation relative to their control group ( p < .005). Furthermore, the model revealed distinct patterns of neuroanatomical deviations for the two diseases, consistent with the existing neuroimaging literature. We conclude that the deep autoencoder provides a flexible and promising framework for assessing total and regional neuroanatomical deviations in neuropsychiatric populations. |
| Author | Pinaya, Walter H. L. Mechelli, Andrea Sato, João R. |
| AuthorAffiliation | 3 Department of Psychosis Studies Institute of Psychiatry, Psychology & Neuroscience, King's College London London UK 2 Center for Engineering, Modeling and Applied Social Sciences Universidade Federal do ABC São Bernardo do Campo SP Brazil 1 Center of Mathematics, Computing, and Cognition Universidade Federal do ABC São Bernardo do Campo SP Brazil |
| AuthorAffiliation_xml | – name: 1 Center of Mathematics, Computing, and Cognition Universidade Federal do ABC São Bernardo do Campo SP Brazil – name: 2 Center for Engineering, Modeling and Applied Social Sciences Universidade Federal do ABC São Bernardo do Campo SP Brazil – name: 3 Department of Psychosis Studies Institute of Psychiatry, Psychology & Neuroscience, King's College London London UK |
| Author_xml | – sequence: 1 givenname: Walter H. L. orcidid: 0000-0003-3739-1087 surname: Pinaya fullname: Pinaya, Walter H. L. email: walhugolp@gmail.com organization: Institute of Psychiatry, Psychology & Neuroscience, King's College London – sequence: 2 givenname: Andrea surname: Mechelli fullname: Mechelli, Andrea organization: Institute of Psychiatry, Psychology & Neuroscience, King's College London – sequence: 3 givenname: João R. surname: Sato fullname: Sato, João R. organization: Universidade Federal do ABC |
| BackLink | https://www.ncbi.nlm.nih.gov/pubmed/30311316$$D View this record in MEDLINE/PubMed |
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| SubjectTerms | Adult Anatomy Artificial intelligence Artificial neural networks Autism autism spectrum disorder Autism Spectrum Disorder - diagnostic imaging Black boxes Brain Brain - diagnostic imaging Brain architecture computational psychiatry deep autoencoder Deep Learning Deviation Disease control Disorders Female Humans Image Interpretation, Computer-Assisted - methods Investigations Learning algorithms Machine learning Magnetic resonance imaging Magnetic Resonance Imaging - methods Male Medical imaging Mental disorders Neural networks Neuroimaging Neuroimaging - methods Neurology Regional analysis Schizophrenia Schizophrenia - diagnostic imaging structural MRI |
| Title | Using deep autoencoders to identify abnormal brain structural patterns in neuropsychiatric disorders: A large‐scale multi‐sample study |
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