Forecasted Brain Structure Alteration Facilitates Cognitive Impairment Prediction: a 7-Year Community Elderly Follow-up Study

Motivation: Predicting the conversion from cognitive normal to mild cognitive impairment (MCI) at the asymptomatic stage is challenging. Goal(s): To investigate whether longitudinal alteration in brain structure can provide valuable information for normal-to-MCI conversion prediction. Approach: We conducted a 7-year longitudinal study on 222 community-dwelling elderly and built a two-stage intelligent prediction model to establish the mapping relationship from the baseline to 7-year changes, using brain T1 MRI scans and comprehensive neuropsychological tests. Results: The model, with forecasted 7-year changed features, achieved promising conversion predictions (accuracy=73.8%). Cortical thickness changes, particularly in the visual and default mode cortices, played a significant role in prediction. Impact: Long-term (7-year) brain cortical changes could be predicted and further used to help disease progression prediction for community elderly. Leveraging pre-built predictive model, physicians can evaluate the progression risk at the preclinical phase, well before the onset of Alzheimer's disease.

Authors

Publication Details

Journal
Proceedings on CD-ROM - International Society for Magnetic Resonance in Medicine. Scientific Meeting and Exhibition/Proceedings of the International Society for Magnetic Resonance in Medicine, Scientific Meeting and Exhibition
Published
2024-11-26
DOI
https://doi.org/10.58530/2024/3874
Primary Topic
Health, Environment, Cognitive Aging
Type
article
Field-Weighted Citation Impact
0.00
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article

Forecasted Brain Structure Alteration Facilitates Cognitive Impairment Prediction: a 7-Year Community Elderly Follow-up Study

Qiufeng Chen, Ling Yue, Han Zhang
Proceedings on CD-ROM - International Society for Magnetic Resonance in Medicine. Scientific Meeting and Exhibition/Proceedings of the International Society for Magnetic Resonance in Medicine, Scientific Meeting and Exhibition
Health, Environment, Cognitive Aging
article

Forecasted Brain Structure Alteration Facilitates Cognitive Impairment Prediction: a 7-Year Community Elderly Follow-up Study

Qiufeng Chen, Ling Yue, Han Zhang
article en

Abstract

Motivation: Predicting the conversion from cognitive normal to mild cognitive impairment (MCI) at the asymptomatic stage is challenging. Goal(s): To investigate whether longitudinal alteration in brain structure can provide valuable information for normal-to-MCI conversion prediction. Approach: We conducted a 7-year longitudinal study on 222 community-dwelling elderly and built a two-stage intelligent prediction model to establish the mapping relationship from the baseline to 7-year changes, using brain T1 MRI scans and comprehensive neuropsychological tests. Results: The model, with forecasted 7-year changed features, achieved promising conversion predictions (accuracy=73.8%). Cortical thickness changes, particularly in the visual and default mode cortices, played a significant role in prediction. Impact: Long-term (7-year) brain cortical changes could be predicted and further used to help disease progression prediction for community elderly. Leveraging pre-built predictive model, physicians can evaluate the progression risk at the preclinical phase, well before the onset of Alzheimer's disease.

Proceedings on CD-ROM - International Society for Magnetic Resonance in Medicine. Scientific Meeting and Exhibition/Proceedings of the International Society for Magnetic Resonance in Medicine, Scientific Meeting and Exhibition
Openalex Percentile: Top 69%
Health, Environment, Cognitive Aging
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Forecasted Brain Structure Alteration Facilitates Cognitive Impairment Prediction: a 7-Year Community Elderly Follow-up Study — Qiufeng Chen, Ling Yue, et al. · Proceedings on CD-ROM - International Society for Magnetic Resonance in Medicine. Scientific Meeting and Exhibition/Proceedings of the International Society for Magnetic Resonance in Medicine, Scientific Meeting and Exhibition (2024) | TGRS Research Map | TGRS