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. 2024 Mar 26;24(1):72.
doi: 10.1186/s12880-024-01250-3.

Classification of cognitive ability of healthy older individuals using resting-state functional connectivity magnetic resonance imaging and an extreme learning machine

Shiying Zhang  1   2   3 , Manling Ge  4   5   6 , Hao Cheng  7   8   9 , Shenghua Chen  7   8 , Yihui Li  7   8 , Kaiwei Wang  9
Affiliations

Classification of cognitive ability of healthy older individuals using resting-state functional connectivity magnetic resonance imaging and an extreme learning machine

Shiying Zhang et al. BMC Med Imaging. .

Abstract

Background: Quantitative determination of the correlation between cognitive ability and functional biomarkers in the older brain is essential. To identify biomarkers associated with cognitive performance in the older, this study combined an index model specific for resting-state functional connectivity (FC) with a supervised machine learning method.

Methods: Performance scores on conventional cognitive test scores and resting-state functional MRI data were obtained for 98 healthy older individuals and 90 healthy youth from two public databases. Based on the test scores, the older cohort was categorized into two groups: excellent and poor. A resting-state FC scores model (rs-FCSM) was constructed for each older individual to determine the relative differences in FC among brain regions compared with that in the youth cohort. Brain areas sensitive to test scores could then be identified using this model. To suggest the effectiveness of constructed model, the scores of these brain areas were used as feature matrix inputs for training an extreme learning machine. classification accuracy (CA) was then tested in separate groups and validated by N-fold cross-validation.

Results: This learning study could effectively classify the cognitive status of healthy older individuals according to the model scores of frontal lobe, temporal lobe, and parietal lobe with a mean accuracy of 86.67%, which is higher than that achieved using conventional correlation analysis.

Conclusion: This classification study of the rs-FCSM may facilitate early detection of age-related cognitive decline as well as help reveal the underlying pathological mechanisms.

Keywords: 10-fold cross-validation; Cognitive test score; Extreme learning machine; Older; Resting-state FC.

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Conflict of interest statement

The authors declare no competing interests.

Figures

Fig. 1
Fig. 1
Study flowchart
Fig. 2
Fig. 2
Flowchart of pre-processing
Fig. 3
Fig. 3
Flowchart of extreme learning machine (ELM) classifier
Fig. 4
Fig. 4
Difference in whole-brain functional connectivity (FC) between older individuals with excellent and poor cognitive test performance. a Average functional connectivity values for the 55 old people with excellent cognitive scores. b Average functional connectivity values for the 43 old people with poor cognitive scores. c Average functional connectivity values for 90 healthy youth. The functional connectivity values are displayed as color-coded matrices in the upper panels and as BrainNet Viewer networks in the below panels. The middle red line in the matrix is the self-correlation for each region
Fig. 5
Fig. 5
Six functional biomarkers (ROIs) of cognitive ability in healthy older. a Difference_mean and Sum_std of values of rs-FCSM for the whole brain. Regions with top 10% of greatest Difference_mean and Sum_std are respectively shown by green dots and red dots, the others are shown by black dots; b ROC curves of ROIs; c ROIs displayed by BrainNet Viewer
See this image and copyright information in PMC

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