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Bolla G, Berente DB, Andrássy A, Zsuffa JA, Hidasi Z, Csibri E, Csukly G, Kamondi A, Kiss M, Horvath AA. Comparison of the diagnostic accuracy of resting-state fMRI driven machine learning algorithms in the detection of mild cognitive impairment. Sci Rep 2023; 13:22285. [PMID: 38097674 PMCID: PMC10721802 DOI: 10.1038/s41598-023-49461-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/12/2023] [Accepted: 12/08/2023] [Indexed: 12/17/2023] Open
Abstract
Mild cognitive impairment (MCI) is a potential therapeutic window in the prevention of dementia; however, automated detection of early cognitive deterioration is an unresolved issue. The aim of our study was to compare various classification approaches to differentiate MCI patients from healthy controls, based on rs-fMRI data, using machine learning (ML) algorithms. Own dataset (from two centers) and ADNI database were used during the analysis. Three fMRI parameters were applied in five feature selection algorithms: local correlation, intrinsic connectivity, and fractional amplitude of low frequency fluctuations. Support vector machine (SVM) and random forest (RF) methods were applied for classification. We achieved a relatively wide range of 78-87% accuracy for the various feature selection methods with SVM combining the three rs-fMRI parameters. In the ADNI datasets case we can also see even 90% accuracy scores. RF provided a more harmonized result among the feature selection algorithms in both datasets with 80-84% accuracy for our local and 74-82% for the ADNI database. Despite some lower performance metrics of some algorithms, most of the results were positive and could be seen in two unrelated datasets which increase the validity of our methods. Our results highlight the potential of ML-based fMRI applications for automated diagnostic techniques to recognize MCI patients.
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Affiliation(s)
- Gergo Bolla
- Neurocognitive Research Center, National Institute of Mental Health, Neurology and Neurosurgery, Budapest, Hungary
- School of PhD Studies, Semmelweis University, Budapest, Hungary
| | - Dalida Borbala Berente
- Neurocognitive Research Center, National Institute of Mental Health, Neurology and Neurosurgery, Budapest, Hungary
- School of PhD Studies, Semmelweis University, Budapest, Hungary
| | - Anita Andrássy
- Neurocognitive Research Center, National Institute of Mental Health, Neurology and Neurosurgery, Budapest, Hungary
| | - Janos Andras Zsuffa
- Neurocognitive Research Center, National Institute of Mental Health, Neurology and Neurosurgery, Budapest, Hungary
- Department of Family Medicine, Semmelweis University, Budapest, Hungary
| | - Zoltan Hidasi
- Department of Psychiatry and Psychotherapy, Semmelweis University, Budapest, Hungary
| | - Eva Csibri
- Department of Psychiatry and Psychotherapy, Semmelweis University, Budapest, Hungary
| | - Gabor Csukly
- Neurocognitive Research Center, National Institute of Mental Health, Neurology and Neurosurgery, Budapest, Hungary
- Department of Psychiatry and Psychotherapy, Semmelweis University, Budapest, Hungary
| | - Anita Kamondi
- Neurocognitive Research Center, National Institute of Mental Health, Neurology and Neurosurgery, Budapest, Hungary
- Department of Neurology, Semmelweis University, Budapest, Hungary
| | - Mate Kiss
- Siemens Healthcare, Budapest, Hungary
| | - Andras Attila Horvath
- Department of Anatomy Histology and Embryology, Semmelweis University, Budapest, Hungary.
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