Supervised machine learning for diagnostic classification from large-scale neuroimaging datasets.
There are growing concerns about the generalizability of machine learning classifiers in neuroimaging. In order to evaluate this aspect across relatively large heterogeneous populations, we investigated four disorders: Autism spectrum disorder (N = 988), Attention deficit hyperactivity disorder (N =...
| Publicado en: | Brain Imaging & Behavior Vol. 14; no. 6; pp. 2378 - 2417 |
|---|---|
| Autores principales: | , , , , , , |
| Formato: | Journal Article |
| Publicado: |
Springer Nature
Dec2020
|
| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=146869385&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 146869385 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 19317557 3GSC jtl: Brain Imaging & Behavior issn: 19317557 maglogo: N pubinfo: dt: Dec2020 vid: 14 iid: 6 pid: 237 pub: Springer Nature place: New York, New York artinfo: ui: 146869385 146869385 NLM31691160 10.1007/s11682-019-00191-8 NLM31691160 146869385 ppf: 2378 ppct: 39 formats: fmt: – @attributes: type: T – @attributes: type: P tig: atl: Supervised machine learning for diagnostic classification from large-scale neuroimaging datasets. aug: au: Lanka, Pradyumna Rangaprakash, D Dretsch, Michael N. Katz, Jeffrey S. Denney, Thomas S. Deshpande, Gopikrishna Denney, Thomas S Jr affil: AU MRI Research Center, Department of Electrical and Computer Engineering, Auburn University, 560 Devall Dr., Suite 266D, 36849, Auburn, AL, USA sug: subj: Neuroradiography Magnetic Resonance Imaging Clinical Assessment Tools Scales Questionnaires ab: There are growing concerns about the generalizability of machine learning classifiers in neuroimaging. In order to evaluate this aspect across relatively large heterogeneous populations, we investigated four disorders: Autism spectrum disorder (N = 988), Attention deficit hyperactivity disorder (N = 930), Post-traumatic stress disorder (N = 87) and Alzheimer's disease (N = 132). We applied 18 different machine learning classifiers (based on diverse principles) wherein the training/validation and the hold-out test data belonged to samples with the same diagnosis but differing in either the age range or the acquisition site. Our results indicate that overfitting can be a huge problem in heterogeneous datasets, especially with fewer samples, leading to inflated measures of accuracy that fail to generalize well to the general clinical population. Further, different classifiers tended to perform well on different datasets. In order to address this, we propose a consensus-classifier by combining the predictive power of all 18 classifiers. The consensus-classifier was less sensitive to unmatched training/validation and holdout test data. Finally, we combined feature importance scores obtained from all classifiers to infer the discriminative ability of connectivity features. The functional connectivity patterns thus identified were robust to the classification algorithm used, age and acquisition site differences, and had diagnostic predictive ability in addition to univariate statistically significant group differences between the groups. A MATLAB toolbox called Machine Learning in NeuroImaging (MALINI), which implements all the 18 different classifiers along with the consensus classifier is available from Lanka et al. (2019) The toolbox can also be found at the following URL: https://github.com/pradlanka/malini . pubtype: Academic Journal doctype: Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
|---|