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Agelink van Rentergem JA, Deserno MK, Geurts HM. Validation strategies for subtypes in psychiatry: A systematic review of research on autism spectrum disorder. Clin Psychol Rev 2021; 87:102033. [PMID: 33962352 DOI: 10.1016/j.cpr.2021.102033] [Citation(s) in RCA: 36] [Impact Index Per Article: 12.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/19/2020] [Revised: 02/14/2021] [Accepted: 04/14/2021] [Indexed: 12/11/2022]
Abstract
Heterogeneity within autism spectrum disorder (ASD) is recognized as a challenge to both biological and psychological research, as well as clinical practice. To reduce unexplained heterogeneity, subtyping techniques are often used to establish more homogeneous subtypes based on metrics of similarity and dissimilarity between people. We review the ASD literature to create a systematic overview of the subtyping procedures and subtype validation techniques that are used in this field. We conducted a systematic review of 156 articles (2001-June 2020) that subtyped participants (range N of studies = 17-20,658), of which some or all had an ASD diagnosis. We found a large diversity in (parametric and non-parametric) methods and (biological, psychological, demographic) variables used to establish subtypes. The majority of studies validated their subtype results using variables that were measured concurrently, but were not included in the subtyping procedure. Other investigations into subtypes' validity were rarer. In order to advance clinical research and the theoretical and clinical usefulness of identified subtypes, we propose a structured approach and present the SUbtyping VAlidation Checklist (SUVAC), a checklist for validating subtyping results.
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Affiliation(s)
- Joost A Agelink van Rentergem
- Department of Psychology, University of Amsterdam, Amsterdam, the Netherlands; Dutch Autism & ADHD Research Center, the Netherlands.
| | - Marie K Deserno
- Department of Psychology, University of Amsterdam, Amsterdam, the Netherlands; Dutch Autism & ADHD Research Center, the Netherlands
| | - Hilde M Geurts
- Department of Psychology, University of Amsterdam, Amsterdam, the Netherlands; Dutch Autism & ADHD Research Center, the Netherlands; Dr. Leo Kannerhuis, the Netherlands
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Li Q, Şentürk D, Sugar CA, Jeste S, DiStefano C, Frohlich J, Telesca D. Inferring Brain Signals Synchronicity from a Sample of EEG Readings. J Am Stat Assoc 2019; 114:991-1001. [PMID: 33100436 DOI: 10.1080/01621459.2018.1518233] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/28/2022]
Abstract
Inferring patterns of synchronous brain activity from a heterogeneous sample of electroencephalograms (EEG) is scientifically and methodologically challenging. While it is intuitively and statistically appealing to rely on readings from more than one individual in order to highlight recurrent patterns of brain activation, pooling information across subjects presents non-trivial methodological problems. We discuss some of the scientific issues associated with the understanding of synchronized neuronal activity and propose a methodological framework for statistical inference from a sample of EEG readings. Our work builds on classical contributions in time-series, clustering and functional data analysis, in an effort to reframe a challenging inferential problem in the context of familiar analytical techniques. Some attention is paid to computational issues, with a proposal based on the combination of machine learning and Bayesian techniques.
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Affiliation(s)
- Qian Li
- Department of Biostatistics, University of California, Los Angeles
| | - Damla Şentürk
- Department of Biostatistics, University of California, Los Angeles.,Department of Statistics, University of California, Los Angeles
| | - Catherine A Sugar
- Department of Biostatistics, University of California, Los Angeles.,Department of Statistics, University of California, Los Angeles.,Department of Psychiatry and Biobehavioral Sciences, University of California, Los Angeles
| | - Shafali Jeste
- Department of Psychiatry and Biobehavioral Sciences, University of California, Los Angeles
| | - Charlotte DiStefano
- Department of Psychiatry and Biobehavioral Sciences, University of California, Los Angeles
| | - Joel Frohlich
- Department of Psychiatry and Biobehavioral Sciences, University of California, Los Angeles
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Automatic clustering using an improved artificial bee colony optimization for customer segmentation. Knowl Inf Syst 2018. [DOI: 10.1007/s10115-018-1162-5] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/18/2022]
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Hasenstab K, Scheffler A, Telesca D, Sugar CA, Jeste S, DiStefano C, Şentürk D. A multi-dimensional functional principal components analysis of EEG data. Biometrics 2017; 73:999-1009. [PMID: 28072468 PMCID: PMC5517364 DOI: 10.1111/biom.12635] [Citation(s) in RCA: 21] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/01/2016] [Revised: 11/01/2016] [Accepted: 11/01/2016] [Indexed: 11/28/2022]
Abstract
The electroencephalography (EEG) data created in event-related potential (ERP) experiments have a complex high-dimensional structure. Each stimulus presentation, or trial, generates an ERP waveform which is an instance of functional data. The experiments are made up of sequences of multiple trials, resulting in longitudinal functional data and moreover, responses are recorded at multiple electrodes on the scalp, adding an electrode dimension. Traditional EEG analyses involve multiple simplifications of this structure to increase the signal-to-noise ratio, effectively collapsing the functional and longitudinal components by identifying key features of the ERPs and averaging them across trials. Motivated by an implicit learning paradigm used in autism research in which the functional, longitudinal, and electrode components all have critical interpretations, we propose a multidimensional functional principal components analysis (MD-FPCA) technique which does not collapse any of the dimensions of the ERP data. The proposed decomposition is based on separation of the total variation into subject and subunit level variation which are further decomposed in a two-stage functional principal components analysis. The proposed methodology is shown to be useful for modeling longitudinal trends in the ERP functions, leading to novel insights into the learning patterns of children with Autism Spectrum Disorder (ASD) and their typically developing peers as well as comparisons between the two groups. Finite sample properties of MD-FPCA are further studied via extensive simulations.
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Affiliation(s)
- Kyle Hasenstab
- Department of Statistics, University of California, Los Angeles, CA 90095, U.S.A
| | - Aaron Scheffler
- Department of Biostatistics, University of California, Los Angeles, CA 90095, U.S.A
| | - Donatello Telesca
- Department of Biostatistics, University of California, Los Angeles, CA 90095, U.S.A
| | - Catherine A. Sugar
- Department of Statistics, University of California, Los Angeles, CA 90095, U.S.A
- Department of Biostatistics, University of California, Los Angeles, CA 90095, U.S.A
- Department of Psychiatry and Biobehavioral Sciences, University of California, Los Angeles, CA 90095, U.S.A
| | - Shafali Jeste
- Department of Psychiatry and Biobehavioral Sciences, University of California, Los Angeles, CA 90095, U.S.A
| | - Charlotte DiStefano
- Department of Psychiatry and Biobehavioral Sciences, University of California, Los Angeles, CA 90095, U.S.A
| | - Damla Şentürk
- Department of Statistics, University of California, Los Angeles, CA 90095, U.S.A
- Department of Biostatistics, University of California, Los Angeles, CA 90095, U.S.A
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